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  • Best AI for OCT: 10 Essential Features Your Platform Must Have 

    best AI for OCT
    Maria Martynova
    8 min.

    Best AI for OCT: 10 Essential Features Your Platform Must Have 

    So you’ve decided to trial AI for OCT analysis and wondering how to choose among all the available platforms. To save you some time, we’ve collected 10 most essential criteria according to which you can assess all existing AI platforms. Using this criteria you will be able to make an informed and rational choice.

    As an ophthalmologist, I am interested in finding innovative and modern approaches that could help me to enhance the workflow and improve patient outcome as a result.Analyzing various platforms, I realized that these 10 criteria are crucial for the right choice.

    1. Regulatory Compliance and Clinical Validation

    In healthcare, safety is always first. Regulatory approval and clinical validation are essential for AI-powered platforms for OCT scan analysis.

    The best AI OCT platforms should meet regulatory standards set by authorities such as the FDA, HIPAA, CE, and ISO. 

    Adhering to regulatory guidelines enhances credibility and fosters trust among healthcare professionals. Check if the AI for OCT analysis tool has all these certificates in place and if they are valid.

    FDA-cleared AI for OCT analysis

    Trial AI for OCT or learn more about it

    Demo Account Get brochure

     

    2.Wide range of biomarkers and pathologies detected

    Some AI for OCT platforms concentrate on certain pathologies, like  Age-Related Macular Degeneration (AMD) or Diabetic Retinopathy, because of the prevalence of these conditions among the population. It mostly means that eye care specialists must know in advance that they are dealing with the AMD patient to find the proof of AMD on the OCT.

    The best AI for OCT tools should have a wide variety of biomarkers and pathologies, including rare ones that cannot be seen daily in clinical practice, such as central retinal vein and artery occlusions, vitelliform dystrophy, macular telangiectasia and others. Altris AI, the leader of OCT for AI analysis, detects 74 biomarkers and pathologies as of today. 

    best AI for OCT

    3.Cloud-Based Data Management and Accessibility

    To ensure seamless integration into clinical workflows, the AI OCT platform should offer cloud-based data management and accessibility. Cloud storage allows for easy retrieval of patient records, remote consultations, and multi-location access. Secure cloud computing also enhances collaboration between ophthalmologists, optometrists, and researchers by enabling data sharing while maintaining compliance with data privacy regulations such as HIPAA and GDPR. 

    Many clinics have strict policies regarding patient data storage as well: it is crucial that the data is stored on the servers in the region of operation. If the clinic is in EU, the data should be stored in the EU.

    4.Real-world usage by eye care specialists

    When choosing the best AI for OCT analysis, real-world usage by eye care specialists is the most critical factor. Advanced algorithms and high accuracy metrics mean little if the AI is not seamlessly integrated into clinical workflows and actively used by optometrists and ophthalmologists. There are thousands of research models available, but when it comes to the implementation, most of them are not available to ECPs.

    Eye care professionals are not IT specialists. They require AI that is intuitive, fast, and reliable. If a system disrupts their workflow, generates excessive false alerts, or lacks clear explanations for its findings, adoption rates will be low—even if the technology itself is powerful. The best AI solutions are those that specialists trust and rely on daily to enhance diagnostic accuracy, streamline patient management, and support decision-making.

    Moreover, real usage generates valuable feedback that continuously improves the AI. Systems actively used in clinical settings undergo rapid validation, refinement, and adaptation to diverse patient populations. This real-world data is far more meaningful than isolated test results in controlled environments.

    5. Customizable Reporting and Visualization Tools

    Reports are the result of the whole AI for OCT scan analysis that is why customizable and comprehensive reports are a must.

    A high-quality AI OCT platform must offer customizable reporting and visualization tools. Clinicians should be able to adjust parameters, select specific data points, and generate detailed reports tailored to individual patient needs.

    Heatmaps, 3D reconstructions, and trend analysis graphs should be available to help visualize disease progression. These tools improve the interpretability of AI-generated insights and facilitate patient education.

    FDA-cleared AI for OCT analysis

    Trial AI for OCT or learn more about it

    Demo Account Get brochure

     

    6.AI for Early Glaucoma Detection

    Glaucoma is a leading cause of irreversible blindness, and since OCT is widely used to assess the retinal nerve fiber layer (RNFL), Ganglion Cell Complex ( GCC), optic nerve head (ONH), AI can significantly enhance early detection and risk assessment.

    Therefore, the best AI for OCT analysis tools have an AI for early glaucoma detection module available to assess the risk of glaucoma especially at the early stage. Moreover, tracking the progression of glaucoma with the help of AI should also be available for eye care specialists.  

    Clear and bright notifications about glaucoma risk are also vital for making AI glaucoma modules easy to use.  AI can provide proactive insights that enable early intervention and personalized treatment plans

    AI to detect glaucoma

    7.User – Friendly Interface and Intuitive Workflow Integration

    A well-designed AI OCT platform should feature a user-friendly interface that integrates seamlessly into existing clinical workflows. 

    It means that even non-tech-savvy eye care specialists should be able to navigate it effortlessly. 

    The interface should be intuitive, reducing the learning curve for healthcare providers. Features such as automated scan interpretation, voice command functionality, and guided step-by-step analysis can enhance usability and efficiency.

    8.Integration with Electronic Health Records (EHRs)

    For a seamless clinical experience, the AI OCT platform should integrate with existing electronic health record (EHR) systems. Automated data synchronization between AI analysis and patient records enhances workflow efficiency and reduces administrative burden. This feature enables real-time updates, streamlined documentation, and easy access to past diagnostic reports.

    9. Universal AI solutions compatible with all OCT devices

    Uf you want to use AI to analyze OCT, this AI should be trained on data received from various OCT devices and therefore should be applicable with various OCT devices. A vendor-neutral AI tool for OCT analysis provides unmatched advantages over proprietary solutions tied to specific hardware. By working seamlessly with multiple OCT devices, it eliminates the need for costly equipment upgrades and ensures broader accessibility across clinics and hospitals.

    This approach also fosters greater innovation, allowing AI models to continuously improve based on diverse datasets rather than being limited to a single manufacturer’s ecosystem. Vendor-neutral solutions integrate effortlessly into existing workflows, reducing training time and boosting efficiency. Clinicians benefit from unbiased, adaptable technology that prioritizes patient outcomes rather than locking users into restrictive ecosystems.

    10. Cost-Effectiveness and Accessibility

    To maximize its impact, an AI-powered OCT platform should be cost-effective and accessible to a wide range of healthcare providers. Affordable pricing models, including subscription-based or pay-per-use plans, can make AI technology available to smaller clinics and developing regions. Accessibility ensures that AI-driven OCT analysis benefits as many patients as possible, improving global eye health outcomes.

    FDA-cleared AI for OCT analysis

    Trial AI for OCT or learn more about it

    Demo Account Get brochure

    Conclusion

    What is the best  AI for OCT scan analysis? The best AI for OCT must be a comprehensive, intelligent, and adaptable platform that enhances diagnostic accuracy, streamlines clinical workflows, and supports proactive eye care. Key features such as high-accuracy automated analysis, multi-modal imaging integration, real-time decision support, cloud-based data management, interoperability, and explainable AI decision-making are crucial for an effective OCT AI system. By incorporating these attributes, AI-driven OCT platforms can revolutionize ophthalmology, enabling early disease detection, personalized treatment planning, and improved patient outcomes. As AI technology continues to advance, its integration with OCT will play an increasingly vital role in shaping the future of eye care.

     

  • Future of Ophthalmology: 2025 Top Trends

    future of ophthalmology
    Maria Znamenska
    13.03.2025
    12 min read

    Future of Ophthalmology: 2025 Top Trends

    In a recent survey conducted by our team, we asked eye care specialists to identify the most transformative trends in ophthalmology by 2025. The results highlighted several key areas, with artificial intelligence (AI) emerging as the clear frontrunner, cited by 78% of respondents.

    future of Ophthalmology

    However, the survey also underscored the significant impact of optogenetics, novel AMD/GA therapies, and the continuing evolution of anti-VEGF treatments. This article will explore the practical implications of these advancements, providing an overview of how they are poised to reshape diagnosis, treatment, research, and, ultimately, patient outcomes in ophthalmology.

    FDA-cleared AI for OCT analysis

    Demo Account Get brochure

     

    Building upon the survey’s findings, we begin with the most prevalent trend:

    AI in Ophthalmology

    future of opthalmology

    AI in Clinical Eye Care Practice

    With the increasing prevalence of conditions like diabetic retinopathy and age-related macular degeneration, there is a growing need for efficient and accurate screening tools. And AI is already valuable for eye-care screening: algorithms can analyze retinal images and OCT scans to identify signs of these diseases, enabling early detection and timely intervention.

    future of ophthalmology

    Source

    AI-powered screening tools can also help identify rare inherited retinal dystrophies, such as Vitelliform dystrophy and Macular telangiectasia type 2. These conditions can be challenging to diagnose, but AI algorithms can analyze retinal images to detect subtle signs that human observers may miss.

    future of ophthalmology

    Biomarkers of MacTel 2 detected and visualized by AI for OCT platform, Altris AI

    AI also starts to play a crucial role in glaucoma management. Early detection of glaucoma demands exceptional precision, as the early signs are often subtle and difficult to detect. Another significant challenge in glaucoma screening is the high rate of false positive referrals, which can lead to unnecessary appointments in secondary care and cause anxiety for patients—yet delayed or missed detection of glaucoma results in irreversible vision loss for millions of people worldwide. So, automated AI-powered glaucoma analysis can offer transformative potential to improve patient outcomes.

    One example of promising AI technology is Altris AI, artificial intelligence for OCT scan analysis, which has introduced its Advanced Optic Disc (OD) Analysis that provides a comprehensive picture of the optic disc’s structural damage, allowing detailed glaucoma assessment for treatment choice and monitoring.

    AI for Glaucoma Detection

    This OD module evaluates optic disc parameters using OCT, providing personalized assessments by accounting for individual disc sizes and angle of rim absence. Such a tailored approach eliminates reliance on normative databases, making evaluations more accurate and patient-specific.

    Furthermore, it enables cross-evaluation across different OCT systems, allowing practitioners to analyze macula and optic disc pathology, even when data originates from multiple OCT devices. Key parameters evaluated by Altris AI’s Optic Disc Analysis include disc area, cup area, cup volume, minimal and maximum cup depth, cup/disc area ratio, rim absence angle, and disc damage likelihood scale (DDLS).

    future of ophthalmology

     

    AI for Clinical Trials and Research

    AI is revolutionizing clinical trials and research in ophthalmology. One such key application of AI is biomarker discovery and analysis. Algorithms can analyze large datasets of medical images, such as OCT scans, to identify and quantify biomarkers for various eye diseases. These biomarkers can be used to assess disease progression, monitor treatment response, and predict clinical outcomes.

    AI is also being used to improve the efficiency and effectiveness of clinical trials. By automating the process of identifying eligible patients for clinical trials, AI can help researchers recruit participants more quickly and ensure that trials include appropriate patient populations, accelerating the development of new treatments.

    future of ophthalmology

    Algorithms can analyze real-world data (RWD) collected from electronic health records and other sources to generate real-world evidence (RWE). RWE provides valuable insights into disease progression, treatment patterns, and long-term outcomes in everyday clinical settings, complementing the findings of traditional randomized controlled trials.

    Oculomics

    Integrating digitized big data and computational power in multimodal imaging techniques has presented a unique opportunity to characterize macroscopic and microscopic ophthalmic features associated with health and disease, a field known as oculomics. To date, early detection of dementia and prognostic evaluation of cerebrovascular disease based on oculomics has been realized. Exploiting ophthalmic imaging in this way provides insights beyond traditional ocular observations.

    future of ophthalmology

    For example, the NeurEYE research program, led by the University of Edinburgh, is using AI to analyze millions of anonymized eye scans to identify biomarkers for Alzheimer’s disease and other neurodegenerative conditions. This research can potentially revolutionize early detection and intervention for these devastating diseases.

    Another effort spearheaded by researchers from Penn Medicine, Penn Engineering is exploring the use of AI to analyze retinal images for biomarkers indicative of cardiovascular risk. AI systems are being trained on fundus photography to detect crucial indicators, such as elevated HbA1c levels, a hallmark of high blood sugar, and a significant risk factor for both diabetes and cardiovascular diseases.

    future of ophthalmology

    Source

    AI analysis of retinal characteristics, such as retinal thinning, vascularity reduction, corneal nerve fiber damage, and eye movement, has shown promise in predicting Neurodegenerative diseases. Specifically, decreases in retinal vascular fractal dimension and vascular density have been identified as potential biomarkers for early cognitive impairment, while reductions in the retinal arteriole-to-venular ratio correlate with later stages.

    Moving from AI, we now turn to another significant trend identified in our survey:

    Optogenetics

    Optogenetics represents a significant leap forward in ophthalmic therapeutics, offering a potential solution for vision restoration in patients with advanced retinal degenerative diseases, where traditional gene therapy often falls short. While gene replacement therapies are constrained by the need for viable target cells and the complexity of multi-gene disorders like retinitis pigmentosa (RP), optogenetics offers a broader approach.

    future of ophthalmology

    This technique aims to circumvent the loss of photoreceptors by introducing light-sensitive proteins, known as opsins, into the surviving inner retinal cells and optic nerve, restoring visual function through light modulation. This method is particularly advantageous as it is agnostic to the specific genetic cause of retinal degeneration.

    By delivering opsin genes to retinal neurons, the technology enables the precise manipulation of cellular activity, essentially transforming these cells into new light-sensing units. This approach can bypass the damaged photoreceptor layer, transmitting visual signals directly to the brain.

    Several companies are pioneering advancements in this field. RhyGaze, for example, has secured substantial funding to accelerate the development of its lead clinical candidate, a novel gene therapy designed for optogenetic vision restoration. Their efforts encompass preclinical testing, including pharmacology and toxicology studies, an observational study to define clinical endpoints, and a first-in-human trial to assess safety and efficacy. The success of RhyGaze’s research could pave the way for widespread clinical applications, significantly impacting the treatment of blindness globally.

    future of ophthalmology

    Source

    Nanoscope Therapeutics is also making significant strides with its MCO-010 therapy. This investigational treatment, administered through a single intravitreal injection, delivers the Multi-Characteristic Opsin (MCO) gene, enabling remaining retinal cells to function as new light-sensing cells. Unlike earlier optogenetic therapies that required bulky external devices, MCO-010 eliminates the need for high-tech goggles, simplifying the treatment process and enhancing patient convenience. The ability to restore light sensitivity without external devices represents a major advancement, potentially broadening the applicability of optogenetics to a wider patient population.

    future of ophthalmology

    Source

    Another critical area of innovation highlighted in our survey is the advancement of treatments for AMD and GA.

    New AMD/GA Treatment

    Age-related macular degeneration (AMD) and geographic atrophy (GA) represent a significant challenge in ophthalmology, demanding innovative therapeutic strategies beyond the established anti-VEGF paradigm.

    future of ophthalmology

    Source

    Gene Correction

    Gene editing is emerging as a powerful tool in the fight against AMD and GA, potentially correcting the underlying genetic errors that contribute to these diseases. Essentially, it allows us to make precise changes to a patient’s DNA.

    Traditional gene editing techniques often rely on creating ‘double-strand breaks’ (DSBs) in the DNA at specific target sites, which are like precise cuts in the DNA strand. These cuts are made using specialized enzymes, like CRISPR-Cas9, which act as molecular scissors. While effective, these methods can sometimes introduce unwanted changes at the cut site, such as small insertions or deletions.

    After a DSB is made, the cell’s natural repair mechanisms kick in. There are two main pathways:

    • Non-Homologous End Joining (NHEJ): This is the cell’s quick-fix method. It essentially glues the broken ends back together. However, this process can sometimes introduce errors, leading to small insertions or deletions that can disrupt the gene’s function.
    • Homology-Directed Repair (HDR): This is a more precise repair method. It uses a ‘donor’ DNA template to guide the repair process, ensuring accuracy. However, HDR is more complex and less efficient, especially in non-dividing cells.

    To overcome these limitations of traditional gene editing, researchers have developed more precise techniques:

    • Base Editing: This technique allows scientists to change a single ‘letter’ in the DNA code without creating DSBs.
    • Prime Editing: This advanced technique builds upon CRISPR-Cas9, allowing for a wider range of precise DNA changes. It can correct most disease-causing mutations with enhanced safety and accuracy.
    • CASTs (CRISPR-associated transposases): This method enables larger DNA modifications without creating DSBs, offering a safer approach to genetic correction.

    Why does this matter for AMD and GA? These advancements in gene editing are crucial for addressing the genetic roots of these pathologies. We can potentially develop more effective and targeted therapies by precisely correcting the faulty genes that contribute to these diseases. The technologies are still being researched, but they hold great promise for the future of ophthalmology.

    Cell Reprogramming

    Cell reprogramming offers a novel approach to regenerative medicine, with the potential to replace damaged retinal cells. This technique involves changing a cell’s fate, either in vitro or in vivo. In vitro reprogramming involves extracting cells, reprogramming them in a laboratory, and then transplanting them back into the patient. In vivo reprogramming, which directly reprograms cells within the body, holds particular promise for retinal diseases. This approach has succeeded in preclinical studies, demonstrating the potential to restore vision in conditions like congenital blindness.

    future of ophthalmology

    Vectors and Delivery Methods

    The success of gene therapy relies on efficiently delivering therapeutic genes to target retinal cells. Vectors are essentially delivery vehicles, designed to carry therapeutic genes into cells. These vectors can be broadly classified into two categories: viral and non-viral. Vectors, both viral and non-viral, are crucial for this process.

    Viral vectors are modified viruses that have been engineered to remove their harmful components and replace them with therapeutic genes. They are highly efficient at delivering genes into cells, as they have evolved to do just that. Adeno-associated viruses (AAVs) are the most commonly used viral vectors in ocular gene therapy due to their safety profile and cell-specificity. The diversity of AAV serotypes allows for tailored gene delivery to specific retinal cell types.

    Non-viral vectors, on the other hand, are synthetic systems that don’t rely on viruses. They can be made from lipids, polymers, or even DNA itself. While they may be less efficient than viral vectors, they offer safety and ease of production advantages.

    Advances in vector design, whether viral or non-viral, are focused on enhancing gene expression, cell-specificity, and carrying capacity.

    Now, let’s examine the ongoing evolution of anti-VEGF treatments, a cornerstone of modern retinal care.

    New Anti-VEGF drugs

    The landscape of ophthalmology has undergone a dramatic transformation since the early 1970s when Judah Folkman first proposed the concept of tumor angiogenesis. His idea sparked research that ultimately led to the identification of vascular endothelial growth factor (VEGF) in 1989 and the development of anti-VEGF therapies, revolutionizing the treatment of neovascular eye diseases, dramatically improving outcomes for patients with wet AMD, diabetic retinopathy, and retinal vein occlusions.

    Population-based studies have shown a substantial reduction (up to 47%) in blindness due to wet AMD since the introduction of anti-VEGF therapies. However, significant gaps remain despite this progress, especially regarding treatment durability. Anti-VEGF drugs require frequent intravitreal injections, which can be difficult for patients due to time commitments, financial costs, and potential discomfort. Although newer agents have extended treatment intervals, patient adherence and undertreatment challenges persist in real-world settings. Innovative approaches are being investigated to address these unmet needs to increase drug durability and reduce the treatment burden.

    Tyrosine Kinase Inhibitors

    One approach to increasing treatment durability is using tyrosine kinase inhibitors (TKIs). TKIs are small-molecule drugs that act as pan-VEGF blockers by binding directly to VEGF receptor sites inside cells, offering a different action mechanism than traditional anti-VEGF drugs that target circulating VEGF proteins.

    Currently, TKIs are being investigated as maintenance therapy, primarily in conjunction with sustained-release delivery systems. Two promising TKIs for retinal diseases are axitinib and vorolanib. In a bioresorbable hydrogel implant, Axitinib is being studied for neovascular AMD and diabetic retinopathy. Vorolanib, in a sustained-release delivery system, is also being investigated for neovascular AMD. These TKIs offer the potential for less frequent dosing, reducing the treatment burden for patients.

    Port Delivery System

    The Port Delivery System (PDS) is a surgically implanted, refillable device that provides continuous ranibizumab delivery for up to 6 months. While it’s FDA-approved for neovascular AMD, it’s also being investigated for other retinal diseases, such as diabetic macular edema and diabetic retinopathy.

    future of ophthalmologySource

    Although the PDS faced a voluntary recall due to issues with septum dislodgment, it has returned to the market with modifications. The PDS offers the potential for significantly reduced treatment frequency for a subset of patients. However, challenges remain, including the need for meticulous surgical implantation and the risk of endophthalmitis.

    Nanotechnology

    Nanotechnology offers promising solutions to overcome limitations of current ocular drug delivery. The unique structure of the eye, with its various barriers, poses challenges for drug delivery. Topical administration often fails to achieve therapeutic concentrations, while frequent intravitreal injections carry risks. Nanotechnology can improve drug solubility, permeation, and bioavailability through nanoparticles, potentially extending drug residence time and reducing the need for frequent injections. Several nanoparticle systems, lipid and polymeric, are being studied for ocular drug delivery, offering hope for more effective and less invasive treatments.

    FDA-cleared AI for OCT analysis

    Demo Account Get brochure

    Summing up

    The advancements discussed in this article, encompassing AI, optogenetics, novel AMD/GA therapies, and refined anti-VEGF treatments, collectively signal a transformative era for ophthalmology. As highlighted by the survey results, AI probably encompasses most of the changes by redefining diagnostic and clinical workflows through its capacity for image analysis, biomarker identification, and personalized patient management.

    Optogenetics offers a distinct pathway to vision restoration, bypassing limitations of traditional gene therapy. The progress in AMD/GA treatments, particularly gene editing and cell reprogramming, presents opportunities for targeted interventions. Finally, the evolution of anti-VEGF therapies, with innovations in drug delivery and sustained-release mechanisms, addresses persistent challenges in managing neovascular diseases.

    These developments, driven by technological innovation and clinical research, promise to enhance patient outcomes and reshape the future of ophthalmic care.

popular Posted

  • Best AI for OCT: 10 Essential Features Your Platform Must Have 

    best AI for OCT
    Maria Martynova
    8 min.

    Best AI for OCT: 10 Essential Features Your Platform Must Have 

    So you’ve decided to trial AI for OCT analysis and wondering how to choose among all the available platforms. To save you some time, we’ve collected 10 most essential criteria according to which you can assess all existing AI platforms. Using this criteria you will be able to make an informed and rational choice.

    As an ophthalmologist, I am interested in finding innovative and modern approaches that could help me to enhance the workflow and improve patient outcome as a result.Analyzing various platforms, I realized that these 10 criteria are crucial for the right choice.

    1. Regulatory Compliance and Clinical Validation

    In healthcare, safety is always first. Regulatory approval and clinical validation are essential for AI-powered platforms for OCT scan analysis.

    The best AI OCT platforms should meet regulatory standards set by authorities such as the FDA, HIPAA, CE, and ISO. 

    Adhering to regulatory guidelines enhances credibility and fosters trust among healthcare professionals. Check if the AI for OCT analysis tool has all these certificates in place and if they are valid.

    FDA-cleared AI for OCT analysis

    Trial AI for OCT or learn more about it

    Demo Account Get brochure

     

    2.Wide range of biomarkers and pathologies detected

    Some AI for OCT platforms concentrate on certain pathologies, like  Age-Related Macular Degeneration (AMD) or Diabetic Retinopathy, because of the prevalence of these conditions among the population. It mostly means that eye care specialists must know in advance that they are dealing with the AMD patient to find the proof of AMD on the OCT.

    The best AI for OCT tools should have a wide variety of biomarkers and pathologies, including rare ones that cannot be seen daily in clinical practice, such as central retinal vein and artery occlusions, vitelliform dystrophy, macular telangiectasia and others. Altris AI, the leader of OCT for AI analysis, detects 74 biomarkers and pathologies as of today. 

    best AI for OCT

    3.Cloud-Based Data Management and Accessibility

    To ensure seamless integration into clinical workflows, the AI OCT platform should offer cloud-based data management and accessibility. Cloud storage allows for easy retrieval of patient records, remote consultations, and multi-location access. Secure cloud computing also enhances collaboration between ophthalmologists, optometrists, and researchers by enabling data sharing while maintaining compliance with data privacy regulations such as HIPAA and GDPR. 

    Many clinics have strict policies regarding patient data storage as well: it is crucial that the data is stored on the servers in the region of operation. If the clinic is in EU, the data should be stored in the EU.

    4.Real-world usage by eye care specialists

    When choosing the best AI for OCT analysis, real-world usage by eye care specialists is the most critical factor. Advanced algorithms and high accuracy metrics mean little if the AI is not seamlessly integrated into clinical workflows and actively used by optometrists and ophthalmologists. There are thousands of research models available, but when it comes to the implementation, most of them are not available to ECPs.

    Eye care professionals are not IT specialists. They require AI that is intuitive, fast, and reliable. If a system disrupts their workflow, generates excessive false alerts, or lacks clear explanations for its findings, adoption rates will be low—even if the technology itself is powerful. The best AI solutions are those that specialists trust and rely on daily to enhance diagnostic accuracy, streamline patient management, and support decision-making.

    Moreover, real usage generates valuable feedback that continuously improves the AI. Systems actively used in clinical settings undergo rapid validation, refinement, and adaptation to diverse patient populations. This real-world data is far more meaningful than isolated test results in controlled environments.

    5. Customizable Reporting and Visualization Tools

    Reports are the result of the whole AI for OCT scan analysis that is why customizable and comprehensive reports are a must.

    A high-quality AI OCT platform must offer customizable reporting and visualization tools. Clinicians should be able to adjust parameters, select specific data points, and generate detailed reports tailored to individual patient needs.

    Heatmaps, 3D reconstructions, and trend analysis graphs should be available to help visualize disease progression. These tools improve the interpretability of AI-generated insights and facilitate patient education.

    FDA-cleared AI for OCT analysis

    Trial AI for OCT or learn more about it

    Demo Account Get brochure

     

    6.AI for Early Glaucoma Detection

    Glaucoma is a leading cause of irreversible blindness, and since OCT is widely used to assess the retinal nerve fiber layer (RNFL), Ganglion Cell Complex ( GCC), optic nerve head (ONH), AI can significantly enhance early detection and risk assessment.

    Therefore, the best AI for OCT analysis tools have an AI for early glaucoma detection module available to assess the risk of glaucoma especially at the early stage. Moreover, tracking the progression of glaucoma with the help of AI should also be available for eye care specialists.  

    Clear and bright notifications about glaucoma risk are also vital for making AI glaucoma modules easy to use.  AI can provide proactive insights that enable early intervention and personalized treatment plans

    AI to detect glaucoma

    7.User – Friendly Interface and Intuitive Workflow Integration

    A well-designed AI OCT platform should feature a user-friendly interface that integrates seamlessly into existing clinical workflows. 

    It means that even non-tech-savvy eye care specialists should be able to navigate it effortlessly. 

    The interface should be intuitive, reducing the learning curve for healthcare providers. Features such as automated scan interpretation, voice command functionality, and guided step-by-step analysis can enhance usability and efficiency.

    8.Integration with Electronic Health Records (EHRs)

    For a seamless clinical experience, the AI OCT platform should integrate with existing electronic health record (EHR) systems. Automated data synchronization between AI analysis and patient records enhances workflow efficiency and reduces administrative burden. This feature enables real-time updates, streamlined documentation, and easy access to past diagnostic reports.

    9. Universal AI solutions compatible with all OCT devices

    Uf you want to use AI to analyze OCT, this AI should be trained on data received from various OCT devices and therefore should be applicable with various OCT devices. A vendor-neutral AI tool for OCT analysis provides unmatched advantages over proprietary solutions tied to specific hardware. By working seamlessly with multiple OCT devices, it eliminates the need for costly equipment upgrades and ensures broader accessibility across clinics and hospitals.

    This approach also fosters greater innovation, allowing AI models to continuously improve based on diverse datasets rather than being limited to a single manufacturer’s ecosystem. Vendor-neutral solutions integrate effortlessly into existing workflows, reducing training time and boosting efficiency. Clinicians benefit from unbiased, adaptable technology that prioritizes patient outcomes rather than locking users into restrictive ecosystems.

    10. Cost-Effectiveness and Accessibility

    To maximize its impact, an AI-powered OCT platform should be cost-effective and accessible to a wide range of healthcare providers. Affordable pricing models, including subscription-based or pay-per-use plans, can make AI technology available to smaller clinics and developing regions. Accessibility ensures that AI-driven OCT analysis benefits as many patients as possible, improving global eye health outcomes.

    FDA-cleared AI for OCT analysis

    Trial AI for OCT or learn more about it

    Demo Account Get brochure

    Conclusion

    What is the best  AI for OCT scan analysis? The best AI for OCT must be a comprehensive, intelligent, and adaptable platform that enhances diagnostic accuracy, streamlines clinical workflows, and supports proactive eye care. Key features such as high-accuracy automated analysis, multi-modal imaging integration, real-time decision support, cloud-based data management, interoperability, and explainable AI decision-making are crucial for an effective OCT AI system. By incorporating these attributes, AI-driven OCT platforms can revolutionize ophthalmology, enabling early disease detection, personalized treatment planning, and improved patient outcomes. As AI technology continues to advance, its integration with OCT will play an increasingly vital role in shaping the future of eye care.

     

  • Future of Ophthalmology: 2025 Top Trends

    future of ophthalmology
    Maria Znamenska
    13.03.2025
    12 min read

    Future of Ophthalmology: 2025 Top Trends

    In a recent survey conducted by our team, we asked eye care specialists to identify the most transformative trends in ophthalmology by 2025. The results highlighted several key areas, with artificial intelligence (AI) emerging as the clear frontrunner, cited by 78% of respondents.

    future of Ophthalmology

    However, the survey also underscored the significant impact of optogenetics, novel AMD/GA therapies, and the continuing evolution of anti-VEGF treatments. This article will explore the practical implications of these advancements, providing an overview of how they are poised to reshape diagnosis, treatment, research, and, ultimately, patient outcomes in ophthalmology.

    FDA-cleared AI for OCT analysis

    Demo Account Get brochure

     

    Building upon the survey’s findings, we begin with the most prevalent trend:

    AI in Ophthalmology

    future of opthalmology

    AI in Clinical Eye Care Practice

    With the increasing prevalence of conditions like diabetic retinopathy and age-related macular degeneration, there is a growing need for efficient and accurate screening tools. And AI is already valuable for eye-care screening: algorithms can analyze retinal images and OCT scans to identify signs of these diseases, enabling early detection and timely intervention.

    future of ophthalmology

    Source

    AI-powered screening tools can also help identify rare inherited retinal dystrophies, such as Vitelliform dystrophy and Macular telangiectasia type 2. These conditions can be challenging to diagnose, but AI algorithms can analyze retinal images to detect subtle signs that human observers may miss.

    future of ophthalmology

    Biomarkers of MacTel 2 detected and visualized by AI for OCT platform, Altris AI

    AI also starts to play a crucial role in glaucoma management. Early detection of glaucoma demands exceptional precision, as the early signs are often subtle and difficult to detect. Another significant challenge in glaucoma screening is the high rate of false positive referrals, which can lead to unnecessary appointments in secondary care and cause anxiety for patients—yet delayed or missed detection of glaucoma results in irreversible vision loss for millions of people worldwide. So, automated AI-powered glaucoma analysis can offer transformative potential to improve patient outcomes.

    One example of promising AI technology is Altris AI, artificial intelligence for OCT scan analysis, which has introduced its Advanced Optic Disc (OD) Analysis that provides a comprehensive picture of the optic disc’s structural damage, allowing detailed glaucoma assessment for treatment choice and monitoring.

    AI for Glaucoma Detection

    This OD module evaluates optic disc parameters using OCT, providing personalized assessments by accounting for individual disc sizes and angle of rim absence. Such a tailored approach eliminates reliance on normative databases, making evaluations more accurate and patient-specific.

    Furthermore, it enables cross-evaluation across different OCT systems, allowing practitioners to analyze macula and optic disc pathology, even when data originates from multiple OCT devices. Key parameters evaluated by Altris AI’s Optic Disc Analysis include disc area, cup area, cup volume, minimal and maximum cup depth, cup/disc area ratio, rim absence angle, and disc damage likelihood scale (DDLS).

    future of ophthalmology

     

    AI for Clinical Trials and Research

    AI is revolutionizing clinical trials and research in ophthalmology. One such key application of AI is biomarker discovery and analysis. Algorithms can analyze large datasets of medical images, such as OCT scans, to identify and quantify biomarkers for various eye diseases. These biomarkers can be used to assess disease progression, monitor treatment response, and predict clinical outcomes.

    AI is also being used to improve the efficiency and effectiveness of clinical trials. By automating the process of identifying eligible patients for clinical trials, AI can help researchers recruit participants more quickly and ensure that trials include appropriate patient populations, accelerating the development of new treatments.

    future of ophthalmology

    Algorithms can analyze real-world data (RWD) collected from electronic health records and other sources to generate real-world evidence (RWE). RWE provides valuable insights into disease progression, treatment patterns, and long-term outcomes in everyday clinical settings, complementing the findings of traditional randomized controlled trials.

    Oculomics

    Integrating digitized big data and computational power in multimodal imaging techniques has presented a unique opportunity to characterize macroscopic and microscopic ophthalmic features associated with health and disease, a field known as oculomics. To date, early detection of dementia and prognostic evaluation of cerebrovascular disease based on oculomics has been realized. Exploiting ophthalmic imaging in this way provides insights beyond traditional ocular observations.

    future of ophthalmology

    For example, the NeurEYE research program, led by the University of Edinburgh, is using AI to analyze millions of anonymized eye scans to identify biomarkers for Alzheimer’s disease and other neurodegenerative conditions. This research can potentially revolutionize early detection and intervention for these devastating diseases.

    Another effort spearheaded by researchers from Penn Medicine, Penn Engineering is exploring the use of AI to analyze retinal images for biomarkers indicative of cardiovascular risk. AI systems are being trained on fundus photography to detect crucial indicators, such as elevated HbA1c levels, a hallmark of high blood sugar, and a significant risk factor for both diabetes and cardiovascular diseases.

    future of ophthalmology

    Source

    AI analysis of retinal characteristics, such as retinal thinning, vascularity reduction, corneal nerve fiber damage, and eye movement, has shown promise in predicting Neurodegenerative diseases. Specifically, decreases in retinal vascular fractal dimension and vascular density have been identified as potential biomarkers for early cognitive impairment, while reductions in the retinal arteriole-to-venular ratio correlate with later stages.

    Moving from AI, we now turn to another significant trend identified in our survey:

    Optogenetics

    Optogenetics represents a significant leap forward in ophthalmic therapeutics, offering a potential solution for vision restoration in patients with advanced retinal degenerative diseases, where traditional gene therapy often falls short. While gene replacement therapies are constrained by the need for viable target cells and the complexity of multi-gene disorders like retinitis pigmentosa (RP), optogenetics offers a broader approach.

    future of ophthalmology

    This technique aims to circumvent the loss of photoreceptors by introducing light-sensitive proteins, known as opsins, into the surviving inner retinal cells and optic nerve, restoring visual function through light modulation. This method is particularly advantageous as it is agnostic to the specific genetic cause of retinal degeneration.

    By delivering opsin genes to retinal neurons, the technology enables the precise manipulation of cellular activity, essentially transforming these cells into new light-sensing units. This approach can bypass the damaged photoreceptor layer, transmitting visual signals directly to the brain.

    Several companies are pioneering advancements in this field. RhyGaze, for example, has secured substantial funding to accelerate the development of its lead clinical candidate, a novel gene therapy designed for optogenetic vision restoration. Their efforts encompass preclinical testing, including pharmacology and toxicology studies, an observational study to define clinical endpoints, and a first-in-human trial to assess safety and efficacy. The success of RhyGaze’s research could pave the way for widespread clinical applications, significantly impacting the treatment of blindness globally.

    future of ophthalmology

    Source

    Nanoscope Therapeutics is also making significant strides with its MCO-010 therapy. This investigational treatment, administered through a single intravitreal injection, delivers the Multi-Characteristic Opsin (MCO) gene, enabling remaining retinal cells to function as new light-sensing cells. Unlike earlier optogenetic therapies that required bulky external devices, MCO-010 eliminates the need for high-tech goggles, simplifying the treatment process and enhancing patient convenience. The ability to restore light sensitivity without external devices represents a major advancement, potentially broadening the applicability of optogenetics to a wider patient population.

    future of ophthalmology

    Source

    Another critical area of innovation highlighted in our survey is the advancement of treatments for AMD and GA.

    New AMD/GA Treatment

    Age-related macular degeneration (AMD) and geographic atrophy (GA) represent a significant challenge in ophthalmology, demanding innovative therapeutic strategies beyond the established anti-VEGF paradigm.

    future of ophthalmology

    Source

    Gene Correction

    Gene editing is emerging as a powerful tool in the fight against AMD and GA, potentially correcting the underlying genetic errors that contribute to these diseases. Essentially, it allows us to make precise changes to a patient’s DNA.

    Traditional gene editing techniques often rely on creating ‘double-strand breaks’ (DSBs) in the DNA at specific target sites, which are like precise cuts in the DNA strand. These cuts are made using specialized enzymes, like CRISPR-Cas9, which act as molecular scissors. While effective, these methods can sometimes introduce unwanted changes at the cut site, such as small insertions or deletions.

    After a DSB is made, the cell’s natural repair mechanisms kick in. There are two main pathways:

    • Non-Homologous End Joining (NHEJ): This is the cell’s quick-fix method. It essentially glues the broken ends back together. However, this process can sometimes introduce errors, leading to small insertions or deletions that can disrupt the gene’s function.
    • Homology-Directed Repair (HDR): This is a more precise repair method. It uses a ‘donor’ DNA template to guide the repair process, ensuring accuracy. However, HDR is more complex and less efficient, especially in non-dividing cells.

    To overcome these limitations of traditional gene editing, researchers have developed more precise techniques:

    • Base Editing: This technique allows scientists to change a single ‘letter’ in the DNA code without creating DSBs.
    • Prime Editing: This advanced technique builds upon CRISPR-Cas9, allowing for a wider range of precise DNA changes. It can correct most disease-causing mutations with enhanced safety and accuracy.
    • CASTs (CRISPR-associated transposases): This method enables larger DNA modifications without creating DSBs, offering a safer approach to genetic correction.

    Why does this matter for AMD and GA? These advancements in gene editing are crucial for addressing the genetic roots of these pathologies. We can potentially develop more effective and targeted therapies by precisely correcting the faulty genes that contribute to these diseases. The technologies are still being researched, but they hold great promise for the future of ophthalmology.

    Cell Reprogramming

    Cell reprogramming offers a novel approach to regenerative medicine, with the potential to replace damaged retinal cells. This technique involves changing a cell’s fate, either in vitro or in vivo. In vitro reprogramming involves extracting cells, reprogramming them in a laboratory, and then transplanting them back into the patient. In vivo reprogramming, which directly reprograms cells within the body, holds particular promise for retinal diseases. This approach has succeeded in preclinical studies, demonstrating the potential to restore vision in conditions like congenital blindness.

    future of ophthalmology

    Vectors and Delivery Methods

    The success of gene therapy relies on efficiently delivering therapeutic genes to target retinal cells. Vectors are essentially delivery vehicles, designed to carry therapeutic genes into cells. These vectors can be broadly classified into two categories: viral and non-viral. Vectors, both viral and non-viral, are crucial for this process.

    Viral vectors are modified viruses that have been engineered to remove their harmful components and replace them with therapeutic genes. They are highly efficient at delivering genes into cells, as they have evolved to do just that. Adeno-associated viruses (AAVs) are the most commonly used viral vectors in ocular gene therapy due to their safety profile and cell-specificity. The diversity of AAV serotypes allows for tailored gene delivery to specific retinal cell types.

    Non-viral vectors, on the other hand, are synthetic systems that don’t rely on viruses. They can be made from lipids, polymers, or even DNA itself. While they may be less efficient than viral vectors, they offer safety and ease of production advantages.

    Advances in vector design, whether viral or non-viral, are focused on enhancing gene expression, cell-specificity, and carrying capacity.

    Now, let’s examine the ongoing evolution of anti-VEGF treatments, a cornerstone of modern retinal care.

    New Anti-VEGF drugs

    The landscape of ophthalmology has undergone a dramatic transformation since the early 1970s when Judah Folkman first proposed the concept of tumor angiogenesis. His idea sparked research that ultimately led to the identification of vascular endothelial growth factor (VEGF) in 1989 and the development of anti-VEGF therapies, revolutionizing the treatment of neovascular eye diseases, dramatically improving outcomes for patients with wet AMD, diabetic retinopathy, and retinal vein occlusions.

    Population-based studies have shown a substantial reduction (up to 47%) in blindness due to wet AMD since the introduction of anti-VEGF therapies. However, significant gaps remain despite this progress, especially regarding treatment durability. Anti-VEGF drugs require frequent intravitreal injections, which can be difficult for patients due to time commitments, financial costs, and potential discomfort. Although newer agents have extended treatment intervals, patient adherence and undertreatment challenges persist in real-world settings. Innovative approaches are being investigated to address these unmet needs to increase drug durability and reduce the treatment burden.

    Tyrosine Kinase Inhibitors

    One approach to increasing treatment durability is using tyrosine kinase inhibitors (TKIs). TKIs are small-molecule drugs that act as pan-VEGF blockers by binding directly to VEGF receptor sites inside cells, offering a different action mechanism than traditional anti-VEGF drugs that target circulating VEGF proteins.

    Currently, TKIs are being investigated as maintenance therapy, primarily in conjunction with sustained-release delivery systems. Two promising TKIs for retinal diseases are axitinib and vorolanib. In a bioresorbable hydrogel implant, Axitinib is being studied for neovascular AMD and diabetic retinopathy. Vorolanib, in a sustained-release delivery system, is also being investigated for neovascular AMD. These TKIs offer the potential for less frequent dosing, reducing the treatment burden for patients.

    Port Delivery System

    The Port Delivery System (PDS) is a surgically implanted, refillable device that provides continuous ranibizumab delivery for up to 6 months. While it’s FDA-approved for neovascular AMD, it’s also being investigated for other retinal diseases, such as diabetic macular edema and diabetic retinopathy.

    future of ophthalmologySource

    Although the PDS faced a voluntary recall due to issues with septum dislodgment, it has returned to the market with modifications. The PDS offers the potential for significantly reduced treatment frequency for a subset of patients. However, challenges remain, including the need for meticulous surgical implantation and the risk of endophthalmitis.

    Nanotechnology

    Nanotechnology offers promising solutions to overcome limitations of current ocular drug delivery. The unique structure of the eye, with its various barriers, poses challenges for drug delivery. Topical administration often fails to achieve therapeutic concentrations, while frequent intravitreal injections carry risks. Nanotechnology can improve drug solubility, permeation, and bioavailability through nanoparticles, potentially extending drug residence time and reducing the need for frequent injections. Several nanoparticle systems, lipid and polymeric, are being studied for ocular drug delivery, offering hope for more effective and less invasive treatments.

    FDA-cleared AI for OCT analysis

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    Summing up

    The advancements discussed in this article, encompassing AI, optogenetics, novel AMD/GA therapies, and refined anti-VEGF treatments, collectively signal a transformative era for ophthalmology. As highlighted by the survey results, AI probably encompasses most of the changes by redefining diagnostic and clinical workflows through its capacity for image analysis, biomarker identification, and personalized patient management.

    Optogenetics offers a distinct pathway to vision restoration, bypassing limitations of traditional gene therapy. The progress in AMD/GA treatments, particularly gene editing and cell reprogramming, presents opportunities for targeted interventions. Finally, the evolution of anti-VEGF therapies, with innovations in drug delivery and sustained-release mechanisms, addresses persistent challenges in managing neovascular diseases.

    These developments, driven by technological innovation and clinical research, promise to enhance patient outcomes and reshape the future of ophthalmic care.

  • Altris AI Launches Advanced Optic Disc Analysis for Glaucoma, Complementing GCC Asymmetry Analysis

    Optic disc analysis
    Maria Znamenska
    1 min.

    Altris AI, a leading force in AI for OCT scan analysis that detects the widest range of retina pathologies and biomarkers, launches an advanced glaucoma Optic Disc Analysis module.  

    Early detection of glaucoma demands exceptional precision, as the early signs are often subtle and difficult to detect. A major challenge in glaucoma screening is the high rate of false positive referrals, which can lead to unnecessary appointments in secondary care. This not only burdens healthcare systems but also causes anxiety for patients. Yet delayed or missed detection of glaucoma results in irreversible vision loss for millions of people worldwide. So the need for timely and accurate glaucoma detection has never been so critical in the eye care industry, and automated AI-powered glaucoma analysis will offer a transformative potential to improve outcomes. 

    To address this critical need, Altris AI has introduced its Advanced Optic Disc (OD) Analysis, building on its earlier innovation with Ganglion Cell Complex (GCC) Asymmetry Analysis to enhance the improvements from the Altris AI macula module which has been available for several years.

    Optic disc analysis for glaucoma

    Altris AI’s glaucoma detection journey began with the creation of AI-powered GCC Asymmetry Analysis, designed to detect early risk of glaucoma.

    In February 2025 Altris launched the AI-powered Advanced Optic Disc (OD) Analysis module as OD analysis is regarded as the gold standard for structural glaucoma diagnosis.

    This method provides a comprehensive picture of structural damage and allows detailed glaucoma assessment for treatment choice and monitoring. 

    Optic Disc analysis

    The module evaluates optic disc parameters using OCT, providing personalized assessments by accounting for individual disc sizes and angle of rim absence. This tailored approach eliminates reliance on normative databases, making evaluations more accurate and patient-specific.

    Altris AI’s platform assigns a severity score for optic disc damage on a scale from 1 to 10, offering valuable insights into glaucomatous changes. Furthermore, it enables cross-evaluation across different OCT systems, allowing practitioners to analyze both macula and optic disc pathology, even when data originates from multiple OCT devices.

    Optic Disc Analysis for Glaucoma: Key Parameters 

    • Disc area
    • Cup area
    • Cup volume
    • Minimal Cup depth
    • Maximum Cup depth
    • Cup/Disc area ratio
    • Rim Absence angle
    • Disc-Damage Likelihood Scale (DDLS)

    The Altris AI Glaucoma Module is compatible with various OCT scan protocols, including:

    • 3D OCT optic disc scans
    • 3D OCT horizontal wide scans
    • 3D OCT vertical-wide scans
    • OCT optic disc raster scans

    By combining  GCC Asymmetry and Advanced Optic Disc analysis for glaucoma empower enabling Eyecare practitioners (ECPs) to make faster evaluations and explore a wider range of treatment options. This streamlined approach empowers ECPswith timely, actionable data, ultimately improving patient outcomes and care.

    Dr. Maria Znamenska, MD, PhD, and a Chief Medical Officer at Altris AI, commented:

    “The launch of our Advanced Optic Disc Analysis module marks a pivotal step forward in glaucoma care. By combining the gold standard of optic disc evaluation with AI-powered precision, we’re equipping eye care professionals with the tools to make more accurate and timely diagnosis of this vision-threatening disorder. This innovation not only reduces false positive referrals but also enhances early detection and treatment planning—ensuring better outcomes for patients and optimizing healthcare resources. Together with GCC asymmetry analysis, our platform empowers clinicians to elevate the standard of glaucoma care, offering hope to millions at risk of vision loss.”

     

    About Altris AI

    Altris AI is an artificial intelligence platform for OCT analysis, capable of detecting the widest range of retinal pathologies and biomarkers on the market – more than 70. Leading the way in AI innovation, Altris AI provides transformative solutions that enhance the diagnosis, treatment, and monitoring of retinal diseases, enabling eye care professionals to deliver exceptional patient care.

  • ML Applied to 3D Optic Disc Analysis for Glaucoma Risk Assessment Across Different OCT Scan Protocols Without a Normative Database

    Angelina Hramatik
    14.02.2025
    20 min read

    Machine Learning Applied to 3D Optic Disc Analysis for Glaucoma Risk Assessment Across Different OCT Scan Protocols Without a Normative Database

    1. Introduction

    Glaucoma is one of the leading causes of irreversible blindness worldwide, affecting millions of people annually. The disease is often asymptomatic in its early stages, making timely diagnosis particularly challenging. Early detection of glaucomatous changes is crucial for preventing vision loss and improving long-term patient outcomes. 

    One well-established method for assessing glaucoma is the Disc Damage Likelihood Scale (DDLS), which evaluates structural changes in the optic nerve head (ONH) based on the extent of neuroretinal rim loss. This method categorizes glaucomatous damage severity by analyzing the relationship between the optic cup and neural rim, while also accounting for optic disc size without relying on a normative database. 1, 2, 3, 4. 

    While DDLS is recognized for its reliability and utility in clinical practice, it is not a standalone diagnostic tool. Rather, it is one of several methods used to identify signs of glaucoma, and its implementation is often limited to specific imaging modalities or scan protocols, such as 3D optic disc-only scans or fundus images. 

    In this article, we introduce an enhanced approach to DDLS analysis that overcomes these limitations. We want to present a solution, which is capable of performing DDLS analysis on any OCT scan protocol that captures the optic nerve, including 3D optic disc scans (which provide the most detailed view of the nerve), as well as OCT horizontal and vertical 3D wide scans. By leveraging advanced machine learning models, we achieve unprecedented flexibility and accuracy, ensuring reliable analysis across different scanning protocols and OCT systems. 

    Unlike traditional systems restricted to specific devices or data formats, our solution processes scans from multiple OCT systems. Moreover, it excels in challenging scenarios, providing clinicians with a robust and versatile tool for analyzing potential signs of glaucoma. 

    FDA-cleared AI for OCT analysis

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    A Brief Theoretical Overview 

    Optical coherence tomography (OCT) scans vary in the anatomical regions they capture. One specific type is the optic disc OCT scan (Figure 2), which provides high-resolution imaging of the optic disc and the surrounding optic nerve head (ONH) structures. This scan type is commonly used in glaucoma assessment, as it allows for the evaluation of the optic nerve’s structure, including the neuroretinal rim, optic cup, and surrounding peripapillary retinal nerve fiber layer (RNFL) — key areas affected in glaucomatous damage. 

    disc likelihood damage oct

    Figure 1. Photograph of the retina of the human eye, with overlay diagrams showing the positions and sizes of the macula, fovea, and optic disc (Reference). 

    disc likelihood damage oct

    Figure 2. 6 mm OCT b-scan of the optic nerve head (ONH) region. 

    In contrast, macular OCT scans (Figure 3) focus on the central retina, providing detailed visualization of structures such as the foveal center, retinal layers, and macular biomarkers (such as drusen, hypertransmission, fluids etc). Since the macula is anatomically distinct from the optic nerve head, standard macular scans do not capture the ONH comprehensively. 

    ai oct optic disc analysis

    Figure 3. 6 mm OCT b-scan of the macular region, showing the foveal pit and retinal layers. 

    A more comprehensive scanning approach is 12 mm wide scan OCT (Figure 4), which captures both the macular region and optic nerve head in a single scan. This broader field of view allows for the simultaneous assessment of central retinal structures and optic nerve-related changes, making it valuable for detecting and monitoring conditions that affect both regions, such as glaucoma and other neurodegenerative or vascular retinal diseases. 

    3d wide glaucoma report

    Figure 4. 12 mm wide scan OCT b-scan, which captures both the macular region and part of the optic nerve head.

    2. Results

    2.1. Experiment Setup 

    Brief Method Overview 

    To evaluate the effectiveness of DDLS analysis in assessing glaucoma severity, we designed an experiment comparing results obtained from processing 3D Optic Disc OCT scans and 3D Wide scan OCT scans with the corresponding reports generated by the OCT system. Our method follows four key steps:  

    1. Detecting optic nerve landmarks like Bruch’s Membrane Opening (BMO) points (Eye Keypoints Retrieval / OCT Keypoint Detector Model); 
    2. Segmenting the inner limiting membrane (ILM) (Retina Layers Segmentation Model); 
    3. Reconstructing the neuroretinal rim geometry; 
    4. Applying the Disc Damage Likelihood Scale (DDLS) for classification.  

    The dataset below was used to validate the algorithm. 

    Dataset Used for Validating the Entire Algorithm 

    For validation, we compared our algorithm’s DDLS measurements with the DDLS values generated by the built-in algorithms of the Optopol REVO NX 130 OCT system. This provided a baseline for assessing accuracy and consistency. 

    To validate our approach, we conducted an experiment comparing DDLS metrics derived from: 

    • 3D Optic Disc OCT scans, which are traditionally used for DDLS analysis. 
    • 3D Wide scans, which capture both the macular and optic nerve regions, providing a more comprehensive dataset for analysis. 

    The dataset includes imaging data from 37 patients examined using the Optopol REVO NX 130 OCT system, with each patient undergoing the following protocols on the same day: 

    • 3D Optic Disc OCT (6mm zone): 168 scans 
    • 3D Wide scan (horizontal protocol, 12mm): 128 scans 

    A report was obtained from the 3D Optic Disc OCT scans, containing all parameters calculated by the device. 

    Since no manual annotations are available for these data, our comparison is conducted directly against the device-generated results. 

    The distribution of data was as follows: 

    • Glaucomatous Optic Disc: 21 cases; 
    • Normal Optic Disc: 16 cases. 

    2.2. Final Validation Results: DDLS Accuracy and Error Metrics 

    To evaluate the performance of our DDLS analysis method, we compared its results with the corresponding DDLS values generated by the OCT device’s built-in algorithms. The device reports serve as a reference point for all calculations, meaning the accuracy, MAE/STD values presented below indicate the level of agreement between our method and the device’s measurements. 

    The parameters compared below are the key indicators for glaucoma stage assessment. 

    • The rim-to-disc ratio (RDR) represents the thinnest neuroretinal rim width relative to the vertical optic disc diameter. A lower RDR indicates a more advanced stage of rim thinning, as glaucoma leads to progressive narrowing of the neuroretinal rim due to the loss of ganglion cells axons. 
    • The rim absence angle (RAA) quantifies the extent of neuroretinal rim loss in degrees. It defines the angle where the rim is completely absent, exposing the optic cup. A wider RAA suggests a more severe stage of glaucoma, as it indicates greater rim loss across the disc circumference. 

    Both RDR and RAA provide complementary perspectives on structural optic nerve damage: 

    • RDR measures the smallest remaining rim thickness in proportion to the disc. 
    • RAA evaluates how much of the disc circumference has lost its rim. 

    By considering both parameters together, a more comprehensive assessment of glaucoma severity can be achieved. Based on RDR and RAA, a DDLS stage is assigned, allowing for standardized classification of glaucoma progression. 

    ai oct optic disc analysis

    Table 1. Validation Results of DDLS Analysis on 3D Optic Disc and 3D Wide Scan OCT Scans 

    The table presents validation results comparing 3D Optic Disc OCT scan and 3D Wide scan OCT in DDLS analysis, focusing on Mean Absolute Error (MAE) and Standard Deviation (STD) for key parameters, along with overall DDLS staging accuracy. These metrics are calculated for the rim-to-disc ratio and rim absence angle by comparing their respective values from 3D Optic Disc OCT scans and 3D Wide scans against those from the device reports, providing a precise assessment of deviations from the reference values. 

    Key Observations

    1. Our Goal: Consistency with Device Reports, Not Outperformance

    The experiment does not aim to surpass the device’s accuracy but rather to demonstrate that our method produces results in alignment with the device-generated DDLS reports. 

    The device report serves as a reference, helping to interpret the figures we present, but this does not mean the device’s output is always the absolute truth. 

    2. High DDLS Staging Accuracy for Both Scan Types

    3D Optic Disc OCT scan: 97.3% accuracy in determining DDLS glaucoma stage. 

    3D Wide scan OCT: 94.59% accuracy, demonstrating strong reliability despite a broader scan area and fewer scans capturing the nerve, leading to less available information. 

    Conclusion: 

    • Both types of scans allow the production of clinically reliable DDLS results, but as expected, 3D optic disc scans provide slightly better accuracy due to their higher resolution of the optic nerve head (ONH). 
    • The small accuracy gap and close values for key parameters between the two suggests that 3D wide scan OCT can still be a viable option for glaucoma assessment, despite offering less detailed information about the optic nerve compared to optic disc scans. 

    3. RD Ratio and Rim Absence Angle: High Precision Within Clinical Margins

    RD Ratio (rim-to-disc ratio): 

    • Step size between DDLS stages: 0.1. 
    • Mean Absolute Error (3D Optic Disc OCT scan): 0.008 (significantly smaller than step size). 
    • Mean Absolute Error (3D Wide scan OCT): 0.024 (still relatively small). 

    Conclusion: 

    • Both 3D Optic Disc OCT scan and 3D Wide scan analysis provide high precision in RD ratio calculations. 
    • The small error ensures that stage classification remains reliable, especially in optic disc scans. 

    Rim Absence Angle: 

    • Step size between DDLS stages: Minimum 45°. 
    • Mean Absolute Error (3D Optic Disc OCT scan): 2.2° (very small compared to step size). Mean Absolute Error (3D Wide scan OCT): 4.2° (still well below stage transition threshold). 

    Conclusion: 

    • The method’s margin of error is far smaller than the clinical threshold for stage differentiation, confirming high accuracy in rim loss assessment. 
    • 3D Optic Disc scans again show better precision, reinforcing that they remain the preferred scan type for DDLS.

    4. Our Advantage: Ability to Perform DDLS on Both Scan Types

    • Unlike traditional DDLS implementations, which work only with 3D Optic Disc scans, our method can perform DDLS analysis on both 3D Wide scan and 3D Optic Disc OCTs. 
    • However, 3D Optic Disc OCT remains the preferred method for maximum precision, as it provides a higher-resolution view of the optic nerve. 

    Key Conclusions 

    1. Our method is unique in its ability to process multiple scan types, while still maintaining high accuracy in both cases. 
    2. On 3D Optic Disc scans, we achieve maximum precision, while on 3D Wide scans, we still maintain clinically reliable accuracy. 
    3. Consistency: Across all glaucoma stages, our method produced stable results that closely matched ground truths provided by medical experts. 
    4. Universal Compatibility: The algorithm performed equally well with scans from other manufacturers, demonstrating its versatility and robustness. 

    2.3. Patient Case Studies: DDLS Analysis in Real-World Scenarios 

    Accurate assessment of glaucoma severity relies on precise measurements of optic nerve parameters, such as disc area, rim-to-disc ratio, and rim absence angle. In the following examples, we analyzed four patient cases, including both normal optic discs and glaucomatous eyes, using 3D Optic Disc OCT scan, 3D Wide scan OCT, and device-generated reports as a reference standard. 

    By consolidating individual patient cases into a single comparative table, we can examine the consistency of DDLS analysis across different scan types and highlight key variations that may arise due to differences in scan coverage, segmentation accuracy, and anatomical structure. The following table summarizes the key optic nerve parameters measured for each patient and scan type. 

    AI OCT Optic Disc Analysis

    Table 2. Comparative DDLS Evaluation Across Multiple Patient Cases 

    Key Findings & Interpretation 

    1. High Consistency Between Our Method and Device Reports

    • Across all cases, the DDLS stage remains identical (4 for normal eyes, 7 or 8 for glaucomatous cases) regardless of whether the input scan was 3D Optic Disc OCT or wide scan, and this result corresponds to the device-generated report. 
    • Key optic nerve parameters such as disc area, cup area, and rim area closely align with the device reference, demonstrating strong algorithm performance. 

    2. Minor Variations in Cup and Rim Measurements

    • Cup and rim area values show slight deviations between 3D Optic Disc OCT scans and 3D Wide scan scans, which is expected due to differences in scan coverage and segmentation sensitivity. 
    • For example, in Patient 3 (Glaucoma, Stage 8): 
    • Cup area was 1.86 mm² (3D Optic Disc OCT scan), 1.88 mm² (3D Wide scan), and 1.81 mm² (Device Report). 
    • Rim area was 0.55 mm² (3D Optic Disc OCT scan), 0.53 mm² (3D Wide scan), and 0.58 mm² (Device Report). 
    • These small variations do not affect final DDLS staging but highlight how scan type can introduce subtle segmentation differences.

    3. Rim Absence Angle Varies Slightly but Remains Within Expected Tolerances

    • The rim absence angle shows minor fluctuations across scan types, especially in glaucomatous cases. 
    • Example: In Patient 3 (Stage 8 Glaucoma), the device reported a rim absence angle of 162°, while our algorithm calculated 155° (3D Optic Disc OCT scan) and 151° (3D Wide scan). 
    • Since DDLS categories for severe glaucoma are defined in large increments (e.g., 45°+ thresholds), these small differences do not impact staging accuracy.

    4. 3D Wide scan OCT Provides Comparable Results to 3D Optic Disc OCT scan

    • Despite covering a larger field of view, wide scans produced DDLS staging results consistent with 3D Optic Disc OCT scans and device reports. 
    • In patients with coexisting macular pathologies, 3D Wide scan OCT may provide additional clinical insights while still maintaining high reliability for glaucoma staging. 

    Conclusion: Reliable DDLS Analysis Across Different Scan Types 

    This unified case study analysis confirms that our DDLS analysis algorithm produces highly consistent results across different scan protocols and patient conditions. 

    1. DDLS stage assignment is identical to device reports across all scan types, ensuring high agreement with clinically validated reference values. 
    2. Key optic nerve measurements (disc area, cup area, rim area) are closely aligned across 3D Optic Disc OCT scan, 3D Wide scan, and device reports, reinforcing algorithm accuracy. 
    3. Minor variations in rim absence angle and segmentation metrics do not affect final glaucoma staging, highlighting the algorithm’s robustness. 
    4. 3D Wide scan OCT offers a viable alternative for 3D Optic Disc OCT scans, particularly in cases where both macular and optic nerve regions need simultaneous evaluation. 

    5. Visual Comparison Shows Strong Similarity to Device Reports

    1. The disk and cup boundaries detected by our algorithm closely match those in the device-generated reports, maintaining consistent shapes and anatomical alignment across both 3D Optic Disc and 3D Wide scan OCT scans. 
    2. However, wide scan-based segmentations tend to be slightly rougher, as less structural information is available compared to dedicated optic disc scans. This trade-off is expected due to the broader field of view in wide scans. 

    These findings validate our algorithm’s flexibility, adaptability, and clinical reliability, demonstrating its potential for seamless integration into real-world ophthalmic workflows. 

    2.4. Why Our Approach Stands Out: Key Advantages Over Traditional DDLS Systems 

    While the previous patient case studies demonstrated the accuracy and consistency of our DDLS analysis across different optic disc conditions, another critical advantage of our method is its ability to work seamlessly across various scanning protocols. Unlike traditional device-restricted solutions, our approach supports DDLS assessment on both standard 3D Optic Disc OCT scans and 3D Wide scans with different orientations. 

    The following table illustrates the same patient’s optic nerve head analyzed using three different scanning protocols: 3D Optic Disc OCT scan, 3D Wide scan Horizontal, and 3D Wide scan Vertical. This comparison highlights the method’s adaptability to different scan formats, ensuring reliable DDLS analysis regardless of the scanning protocol used. This example is taken from a Topcon Maestro 2 OCT system, providing an additional reference for processing across different OCT systems. 

    AI OCT Optic Disc Analysis

    Table 3. Comparative DDLS Analysis Across Different Scanning Protocols: 3D Optic Disc OCT, 3D Wide scan Horizontal, and 3D Wide scan Vertical. 

    This capability significantly enhances clinical applicability, allowing our algorithm to process data from various scanning protocols and devices while maintaining high accuracy. The ability to analyze both 3D Optic Disc and 3D Wide scan OCT scans — across different orientations and machine types — ensures comprehensive glaucoma assessment even in cases where scan availability or quality may vary. 

    Key advantages over traditional DDLS analysis methods 

    1. Device Independence

    1. While most existing solutions are restricted to proprietary OCT data formats, our algorithm processes scans from any OCT system, ensuring broad compatibility across devices. 

    2. Consistent Accuracy Across Different Scan Types 

    1. Our algorithm closely matches device-generated DDLS reports, achieving 97.3% accuracy for 3D Optic Disc OCT scans and 94.59% for 3D Wide scan OCTs. 
    2. Patient cases confirm this consistency, with both normal and glaucomatous eyes correctly classified, even when analyzed with different scan types. 

    3. Robust Performance in Edge Cases 

    1. Unlike traditional device-based DDLS assessments, which may struggle with low-quality images or atypical anatomical features, our approach maintains high accuracy in challenging clinical scenarios. 
    2. Patient examples with small optic discs and advanced-stage glaucoma demonstrated that our algorithm successfully identified key DDLS indicators even when scan quality or nerve structure was less distinct. 

    4. Expanded Assessment Through 3D Wide scan OCT 

    1. The ability to perform DDLS analysis on Horizontal and Vertical 3D Wide scans allows for a more comprehensive evaluation by incorporating both macular and optic nerve data. 
    2. In patients with coexisting macular pathologies, wide scans enabled earlier detection of glaucomatous changes that would have been missed if only optic disc scans were used. 

    3. Detailed Approach Description

    To assess glaucoma stage on OCT scans using DDLS analysis, the following steps should be performed: 

    1. Optic Nerve Landmarks Detection – Localization of the optic nerve in the b-scan view of each scan by identifying key anatomical landmarks. 
    2. ILM DetectionSegmentation of the inner limiting membrane (ILM) in the b-scan view of each scan to establish a reference for neuroretinal rim measurement. 
    3. Neuroretinal Rim Reconstruction – Construction of the neuroretinal rim geometry based on detected nerve landmarks and ILM segmentation. 
    4. DDLS Analysis – Application of the Disc Damage Likelihood Scale (DDLS) to assess glaucoma severity based on neuroretinal rim measurements. This includes assigning a DDLS stage according to rim width and optic disc size, with a focus on detecting localized thinning and asymmetry. 

    3.1. Keypoint Annotation Process / Nerve Detection 

    The foundation of our approach lies in a high-quality, annotated dataset meticulously labeled by a team of four expert ophthalmologists. The annotation process focused on identifying key anatomical landmarks in both the macular region and the optic disc nerve zones, both of which are critical for detecting glaucomatous changes and performing Disc Damage Likelihood Scale (DDLS) analysis. 

    These keypoints serve as essential data for evaluating disease progression and training machine learning models. The dataset was carefully selected based on key clinical features, such as the presence or absence of nerve fibers, foveal pits, and other pathological markers, ensuring a comprehensive representation of various conditions and scan types. 

    The annotated dataset consists of approximately 370 unique OCT examinations with more than 56,000 b-scans, covering a range of physical scanning areas, pathology types, and optic nerve conditions to enhance the model’s robustness. The scans are categorized as follows: 

    • Optic Disc with no excavation: ~15 examinations; 
    • Glaucomatous Optic Disc: ~105 examinations; 
    • Normal Optic Disc: ~105 examinations; 
    • Wide scans (covering both the macular and optic nerve regions): ~60 examinations; 
    • Normal Retina Scans: ~40 examinations; 
    • Pathological Retina Scans: ~45 examinations. 

    This detailed annotation process ensures high precision and reliability, enabling the algorithm to generalize across diverse cases while maintaining clinical accuracy in real-world scenarios. 

    3.2. Eye Keypoints Retrieval / OCT Keypoint Detector 

    Our keypoint detection model represents a logical evolution of the model for exam center detection, designed to efficiently and accurately identify key anatomical landmarks in OCT scans. The architecture integrates elements from UNet 5 and CenterNet 6, incorporating YOLO-inspired 7 techniques for keypoint prediction. Additionally, the backbone has been adapted to a transformer-based model 8, enhancing feature extraction capabilities. 

    Training Process 

    The training process follows a multi-stage approach, ensuring robustness, accuracy, and efficiency: 

    1. Stage 1: Detects general keypoints, establishing a foundation for precise landmark localization. 
    2. Stage 2: Groups and refines the identification of specific keypoints, progressively improving the model’s understanding of anatomical structures. 

    This structured approach enhances the model’s reliability across different scan types while maintaining computational efficiency. 

    Key Features 

    Data Preprocessing 

    • The data is augmented using unsupervised techniques, leveraging libraries such as Albumentations 9 to introduce variations such as rotations, scaling, and noise addition. 
    • This ensures the model encounters a wider variety of real-world scenarios during training, improving its generalization capability. 

    Training Process 

    • The model is trained using supervised learning techniques, optimizing a loss function through backpropagation and gradient descent. 
    • This approach allows for continuous refinement and adaptation to complex variations in OCT scans. 

    Parameterization & Tuning 

    • The model includes millions of adjustable parameters (weights), which are fine-tuned to increase accuracy. 
    • Key hyperparameters such as learning rate, batch size, and network depth are carefully selected to maximize performance. 
    • Advanced optimization techniques, including grid search, random search, and Bayesian optimization, are used to find the best hyperparameter configuration. 

    3.3. Retina Layers Segmentation Model 

    The Retina Layers Segmentation Model is our production-stage model, actively used within the Altris AI platform. It was incorporated into this experiment without modifications, ensuring that the results reflect real-world performance as seen in our deployed system. 

    Our Retina Layers Segmentation Model enables precise segmentation of key retinal layers in OCT scans, crucial for detecting structural changes linked to glaucoma and other retinal diseases. The model identifies: 

    • ILM, RNFL, GCL, IPL, INL, OPL, ONL, ELM, MZ, EZ, OS, RPE, BM 

    The training dataset consists of 5,000 expert-annotated OCT b-scans, covering a diverse range of patient demographics, including different ages and ethnic backgrounds. The segmentation model is designed to detect and delineate key retinal layers with high accuracy. 

    Training & Architecture 

    The model is based on U-Net with a ResNet backbone, optimized for OCT images. Training includes: 

    • Expert Annotation: Medical specialists labeled layers for ground truth. 
    • Augmentation: Albumentations-based transformations enhance robustness. 
    • Supervised Learning: Predicts segmentation masks using backpropagation. 
    • Hyperparameter Optimization: Grid search, random search, and Bayesian tuning maximize performance. 

    Model Validation & Performance 

    • The model was validated using a holdout validation approach, with separate validation and test sets that were not exposed during training. 
    • Real-world testing was conducted using scans from various clinical settings to ensure robustness. 
    • Performance was evaluated using the Mean Dice Coefficient across all layers, achieving a score of 0.80, with layer-specific scores ranging from 0.63 to 0.92, confirming high segmentation accuracy. 
    • Cross-domain testing demonstrated consistent performance across different OCT systems, and stability was confirmed over scans collected across different time periods. 

    This efficient, accurate, and generalizable model strengthens DDLS analysis and enhances AI-driven retinal diagnostics. 

    3.4. DDLS Algorithm 

    The DDLS algorithm evaluates glaucomatous changes by analyzing the geometric relationship between the neural rim and optic cup in the optic nerve head. Key steps include: 

    1. Localization: Identifying boundaries of the optic cup and neuroretinal rim by reconstructing geometry on a b-scan view using disc landmarks and an inner limiting membrane.

    3d wide glaucoma report

    Figure 5. B-scan Geometry Visualization. 

    1. Measurement: Calculating the DDLS stage based on the ratio between the rim and disc boundaries.
    2. Cross-Scan Application: Adapting the analysis for 3D Wide scans (both Horizontal and Vertical protocols) as well as 3D Optic Disc-specific scans.

    Our implementation enhances this traditional method by leveraging wide scans, enabling a more comprehensive assessment of glaucomatous changes. 

    3.5. Evaluation 

    To ensure the reliability and effectiveness of our DDLS algorithm, we conducted a rigorous evaluation process, adhering to best practices in data usage, ethics, and performance validation. 

    Data Integrity 

    • Measures were implemented to prevent data leakage, ensuring that scans from the same patient did not appear in both training and testing sets. 

    Ethical Considerations 

    • The analysis strictly relies on OCT-related data (e.g., scan zone size, laterality, pixel spacing) without incorporating any personal patient information. 

    Performance Metrics 

    • Keypoint detection accuracy was evaluated using Mean Squared Error (MSE), comparing model-predicted keypoints with expert annotations. 
    • Additional metrics included correctness of scan center-related landmarks and accuracy in the optic nerve region, ensuring precision in clinical applications. 

    The evaluation results confirmed the algorithm’s robustness, demonstrating significant performance gains, particularly in edge cases, where traditional methods often struggle. 

    Discussion 

    Our DDLS analysis method represents a significant advancement in glaucoma detection. Key benefits include: 

    1. Universal Compatibility: The ability to process data from various devices ensures broad applicability. 
    2. Enhanced Accuracy: By incorporating data from both macular and optic nerve regions, our approach captures more subtle glaucomatous changes. 
    3. Edge Case Performance: Advanced machine learning techniques enable accurate analysis even in challenging scenarios. 

    Compared to traditional methods, our system provides a more flexible, reliable, and comprehensive solution for early glaucoma detection. 

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    Conclusion 

    By integrating 3D Wide scans and state-of-the-art machine learning models, we have enhanced DDLS analysis for glaucoma detection, ensuring high accuracy, broad compatibility, and robustness across diverse clinical scenarios. 

    Unlike traditional solutions, our algorithm: 

    1. Works across multiple OCT devices, eliminating the constraints of proprietary data formats. 
    2. It closely matches device-generated DDLS reports, achieving 97.3% accuracy for 3D Optic Disc OCT scans and 94.59% for 3D Wide scans. 
    3. Performs reliably in edge cases, such as small optic discs and advanced-stage glaucoma, where traditional methods may struggle. 
    4. Supports both Horizontal and Vertical 3D Wide scans, enabling more comprehensive assessments that incorporate both macular and optic nerve data. 
    5. Enhances early glaucoma detection, particularly in patients with coexisting macular pathologies, where wide scans provide additional clinical insights. 

    By delivering consistently accurate DDLS staging, regardless of scan type or manufacturer, our system establishes a new benchmark for universal glaucoma assessment. This technology has the potential to significantly improve early detection and management, ultimately preserving vision and enhancing patient outcomes. 

    References 

    1. Spaeth, G. L. (2005). The Disc Damage Likelihood Scale. Glaucoma Today. https://glaucomatoday.com/articles/2005-jan-feb/0105_18.html 
    2. Cheng, K. K. W., & Tatham, A. J. (2021). Spotlight on the Disc-Damage Likelihood Scale (DDLS). Clinical Ophthalmology, 15, 4059–4071. https://pmc.ncbi.nlm.nih.gov/articles/PMC8504474/ 
    3. Zangalli, C., Gupta, S. R., & Spaeth, G. L. (2011). The disc as the basis of treatment for glaucoma. Saudi Journal of Ophthalmology, 25(4), 381-387. https://www.sciencedirect.com/science/article/pii/S1319453411000993 
    4. Review of Optometry Staff. (2023, January 23). Optic disc staging systems effective in grading advanced glaucoma. Review of Optometry. https://www.reviewofoptometry.com/article/optic-disc-staging-systems-effective-in-grading-advanced-glaucoma 
    5. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. [Preprint]. Posted May 18, 2015. https://arxiv.org/abs/1505.04597 
    6. Duan K, Bai S, Xie L, et al. CenterNet: Keypoint Triplets for Object Detection. [Preprint]. Posted April 17, 2019. https://arxiv.org/abs/1904.08189 
    7. Redmon J, Divvala S, Girshick R, Farhadi A. You Only Look Once: Unified, Real-Time Object Detection. [Preprint]. Posted June 8, 2015. https://arxiv.org/abs/1506.02640 
    8. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. [Preprint]. Posted October 22, 2020. https://arxiv.org/abs/2010.11929 
    9. Buslaev A, Iglovikov V, Khvedchenya E, et al. Albumentations: Fast and Flexible Image Augmentations. [Preprint]. Posted September 18, 2018. https://arxiv.org/abs/1809.06839
  • Altris AI Introduces Next-Generation Fluids and GA Quantification Features

    Maria Znamenska, MD, PhD Ophthalmology
    1 min. read

    Altris AI Introduces Next-Generation Fluids and GA Quantification Features

    Altris AI, a pioneering force in artificial intelligence for OCT scan analysis, has unveiled additional quantification features for Fluids and Geographic Atrophy (GA) tracking on its web platform. Altris AI currently detects over 70 retina pathologies and biomarkers. However, we have decided to enhance its capabilities by adding additional Fluids and GA quantification and tracking functionalities, recognizing that eye care specialists frequently work with these conditions.

    These advancements empower eye care professionals (ECPs) with cutting-edge tools for diagnosing and managing retinal diseases. By integrating AI-driven quantitative tracking and progression monitoring, Altris AI enables specialists to deliver more personalized and effective treatments, ultimately enhancing patient outcomes.

    Fluids Quantification and Progression Tracking

    The presence of fluids such as Intraretinal Cystoid Fluid (IRC), Diffuse Edema, Subretinal Fluid (SRF), and Serous Retinal Pigment Epithelium (RPE) Detachment are critical biomarkers for conditions like nAMD, DME, DR, and RVO. Accurate detection, quantification, and tracking of these fluids are essential for monitoring disease activity, evaluating treatment efficacy, and making informed prognoses.

    We created specialized more detailed functions which detect these biomarkers for more specific and accurate tracking. The AI algorithm was additionally trained to work directly with fluids taking into account the importance of these biomarkers for accurate diagnostics.

    Altris AI’s advanced algorithms, trained on millions of OCT scans, provide precise and objective fluid analysis. Each of the four fluid types is localized and color-coded for clarity. Quantitative metrics such as volume, area, and ETDRS grids (1, 3, and 6 mm) are calculated and presented in mm3 or nanoliters for comprehensive evaluation. The Progression Tracking feature offers historical trend analysis with intuitive visualizations through graphs and percentages. For instance, if Cystoid Fluid (IRC) increases in volume, ECPs can immediately identify and address the change.

    Precision in Geographic Atrophy (GA) Monitoring

    Recent advancements in GA treatment have led to a growing need for large-scale screening in clinical practice. However, this increased demand often means higher workloads and less time for in-depth analysis. 

    The platform facilitates automated detection, quantification, and tracking of GA by analyzing key biomarkers: Pigment Epithelium (RPE) atrophy, Hypertransmission, Neurosensory Retina Atrophy, and Ellipsoid Zone (EZ) disruption. These biomarkers are color-coded for easier identification. 

    We assess GA using three key criteria:

    1. Overlapping region of 3 biomarkers: Hypertransmission, RPE Atrophy, and Neurosensory Retina Atrophy (referred as the GA zone).
    2. The shortest distance from the Fovea center to the GA zone.
    3. Percentage of the GA zone covering the 1 mm, 3 mm, and 6 mm ETDRS grid areas.

    AI for GA

    We also improved the accuracy of a critical step in our AI pipeline: the fovea and central scan detection. Altris AI’s updated model is much more robust in detecting fovea zone and central scan now. Especially in cases when the center cannot be distinguished due to pathology presence or other reasons, the model is trained to analyze the whole surface and find reference locations from which a central scan could be determined. The new model can find an accurate center in 95% of cases, in other situations, it can efficiently estimate the center location (as opposed to a simple analysis flow used by ECPs where the geometrical center is selected). This advancement significantly enhances the precision of GA detection.

    Further Progression Tracking enhances GA management by visualizing changes over time, supporting timely and accurate treatment decisions. By streamlining workflows and providing actionable insights, this feature helps ECPs make informed choices and potentially preserve vision in GA patients.

    Dr. Maria Znamenska, MD, PhD, and a Chief Medical Officer at Altris AI, commented:

    “We listened to our clients and introduced Fluids and GA tracking features. In 2025, eye care specialists will have the tools to combine their expertise with next-generation AI technology to effectively tackle conditions that threaten vision. Our formula is simple: detect, quantify, and track fluids, GA, and 70+ retina pathologies and biomarkers for better patient outcomes.”

    About Altris AI

    Altris AI is an artificial intelligence platform for OCT analysis that detects the widest range of retina pathologies and biomarkers on the market – more than 70. Leading the way in AI innovation, Altris AI provides transformative solutions that enhance the diagnosis, treatment, and monitoring of retinal diseases, enabling eye care professionals to deliver exceptional patient care.

  • OCT Scan Normal Eye vs 8 Most Common Pathologies

    normal abnormal oct scan
    Maria Znamenska
    31.10.2024
    14 min read

    OCT Scan Normal Eye vs. 8 Most Common Pathologies

    Differentiating between an OCT scan of a normal eye vs. a pathological one is a practical skill gained after years and years of practice. However, educating yourself on the basic differences will speed up the process. Understanding the “why” and “how” behind any changes on the OCT scan, compared to a normal macula OCT, will speed up your learning curve and deepen your expertise as a retinal expert.

    The article’s first part focuses on key OCT features and their meaning as a structural change for retinal architecture. The second part discusses the most recognizable OCT features of eight common pathologies.

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    OCT Scan: Normal Eye

    When evaluating an OCT scan, the most logical step is to understand how a normal macula OCT should look. The most telling feature across all scans is the contrast between light and dark areas. Typically, the nerve fiber layer and the underlying ganglion cell layer appear brighter than the densely packed nuclear layers. This is followed by the inner plexiform layer interface, which presents as a bright, hyperreflective area.

    The inner nuclear layer, composed of densely packed nuclei, appears dark. This is followed by the outer plexiform layer, the outer nuclear layer, and Henle’s layer. The external limiting membrane, an important landmark for assessing retinal health, is also visible. The ellipsoid zone (EZ) is another bright layer, while the interdigitation zone may not always be distinguishable from the underlying RPE layer, even in healthy eyes. Finally, the RPE and inner choroid appear hyperreflective.

    normal macula oct

    Structure

    The ELM and EZ are critical structures to assess. In a normal macula OCT, the distance between the EZ and ELM is shorter than between the EZ and the RPE. The apparent “elevation” of the EZ in the foveal center results from the elongated outer segments of the foveal cones.

    It’s important to remember that not all retinal structures are readily visible on a normal macula OCT. For example, Henle’s fiber layer is more easily distinguished in the presence of retinal pathology, such as swelling or thinning. Similarly, Bruch’s membrane is usually not visualized unless there is a separation between the RPE and Bruch’s membrane, often indicative of disease.

    Thickness

    Choroidal thickness is another key factor in OCT assessment. A general rule of thumb is that the choroid (between the RPE and the outer choroidal boundary) is approximately as thick as the retina. Thinning of the choroid may be observed in myopic or older patients, while marked choroidal thickening can raise suspicion for diseases like central serous retinopathy.  

    The OCT scan also provides information about laterality. The nerve fiber layer is characteristically thicker near the optic nerve head.  Conversely, if the nerve fiber layer is not visualized in its expected location on an otherwise OCT normal scan, it could signal significant nerve fiber layer loss, potentially due to glaucoma or other optic neuropathies.

    Reflectivity

    Specific OCT terminology helps describe scans and differentiate normal findings from pathology.

    Two fundamental concepts in OCT interpretation are hyporeflectivity and hyperreflectivity, which form the basis for understanding the structural composition of the retina as visualized in an OCT scan.

    Hyporeflectivity refers to the increased light transmission capacity of a structure. The OCT scanning laser beam passes through hyporeflective structures with minimal reflection. The quintessential example of a hyporeflective structure is the vitreous humor. It appears as a dark area in the uppermost portion of a normal OCT scan, situated above the retina.

    But hyporeflectivity can also be pathological, deviating from the patterns observed in a normal macula OCT; in the retina, it manifests in three primary ways.

    Like the vitreous, subretinal fluid exhibits high light transmission and appears black on OCT. A uniformly black region suggests the fluid lacks cellular debris or other inclusions.

    normal abnormal oct scan

    Subretinal fluid on OCT

    Fluid can also accumulate within the retinal layers, for example, between the layers of the neuroepithelium. This intraretinal fluid also appears hyporeflective on OCT.

    oct scan normal eye

    Intraretinal fluid on OCT

    Following a degenerative process within the retina, a cavity or void may form where retinal tissue has been lost. These degenerative cavities lack the cellular components necessary to reflect light and thus appear as dark spaces on OCT.  It’s important to differentiate these cavities from cystic spaces, which may have distinct clinical implications.

    One example is outer retinal tubulations. While associated with various diseases, outer retinal tubulations (ORTs) generally indicate outer retinal degeneration and atrophy.

    normal macula oct

    Outer retinal tubulations on OCT

    Hyperreflectivity, unlike hyporeflectivity, indicates structures with high light reflectance. On the grayscale spectrum of an OCT image, hyperreflective structures appear progressively whiter. 

    The retinal pigment epithelium (RPE) complex and Bruch’s membrane are considered the most hyperreflective structures in a normal macula OCT.

    Pathological processes can introduce new hyperreflective elements within the retina, aiding in differentiating normal and abnormal OCT scans. A typical example is hard exudates, frequently observed in diabetic retinopathy. These lipid-rich deposits are extremely dense, causing them to appear bright white on OCT due to the complete reflection of incident light. Furthermore, this high density leads to a shadowing effect beneath the deposits, caused by strong backscattering of the OCT signal.

    normal abnormal oct scan

    Hard exudates and shadowing on OCT

    Epiretinal membranes (ERMs) – a thin membrane or layer of scar tissue that forms over the retina – are also hyperreflective. It is composed of dense connective tissue with high light-reflecting properties and appears white on OCT scans.

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    Integrity

    Beyond hypo- and hyperreflectivity, OCT interpretation involves assessing the structural integrity of retinal layers. For instance, in an OCT scan of a normal eye, Bruch’s membrane appears as a thin, continuous line underlying the retinal pigment epithelium (RPE). The RPE is a monolayer of cells, ideally presenting with a smooth and uniform optical density. However, some pathologies, particularly early stages of age-related macular degeneration (AMD), may show unevenness or integrity loss in the RPE and Bruch’s membrane complex. 

    Disruption of the ellipsoid zone (EZ) is a particularly concerning finding on OCT, often indicating photoreceptor damage. Significant disruption of the EZ in the central macula is a strong biomarker for adverse visual outcomes.

    The closer the loss of integrity extends toward the foveal center, the poorer the visual prognosis tends to be.

    oct scan normal eye

    Ellipsoid zone disruption on OCT

    OCT also plays a crucial role in visualizing and characterizing breaks in the structural integrity of the retina. These breaks, commonly referred to as retinal tears or holes, can be classified as full-thickness or partial-thickness, depending on the extent of retinal involvement.

    Full-thickness breaks completely separate all retinal layers, while partial-thickness breaks involve only some retinal layers. OCT allows for precise delineation of the layers involved and the overall morphology of the break.

    Retinal holes can also be categorized by their location. Macular holes, as the name suggests, involve the central retina and can lead to significant central vision loss and require prompt attention.

    normal macula oct

    Lamellar macular hole on OCT

    Non-macular holes occur outside the central macular region, often in the peripheral retina. While they may not cause immediate central vision disturbances, they can still lead to serious complications, such as retinal detachment, if left untreated.

    Definition

    The blurring of retinal structures, or loss of definition, is another key OCT concept. This loss of the retina’s normal layered organization, seen in diseases like AMD, manifests as indistinct layers merging into a homogenous mass.

    normal macula oct

    Disorganisation of retinal inner layers on OCT

    Hypertransmission in OCT refers to enhanced signal penetration due to reduced blockage of the OCT light signal. This phenomenon is frequently observed in geographic atrophy, a late stage of AMD characterized by the atrophy of the retinal pigment epithelium, choriocapillaris, and photoreceptors.

    normal abnormal oct scanHypertransmission on OCT

    In a normal macula OCT, a signal is attenuated as it traverses the various retinal layers, with a portion of the signal being reflected to the detector. However, in geographic atrophy (GA), the loss of RPE and other retinal structures reduces this attenuation, allowing the OCT signal to penetrate deeper into the choroid. This increased penetration results in a stronger signal return from the choroidal layers, creating essentially a “corridor” of enhanced signal penetration through the atrophic areas of the retina.  This deep penetration and strong signal return, unfortunately, indicate significant retinal damage and are associated with a poor visual prognosis.

    Displacement

    Another term used to describe OCT scan results is elevation. It refers to the upward displacement of retinal structures from their normal anatomical position. In the context of age-related macular degeneration (AMD), elevation is frequently associated with the presence of drusen.

    Drusen are extracellular deposits that accumulate between the retinal pigment epithelium (RPE) and Bruch’s membrane. They are a hallmark of AMD and can vary in size, shape, and composition.  Drusen are typically categorized as hard, soft, or confluent based on their ophthalmoscopic appearance.

    oct scan normal eye

    Hard and soft drusen on OCT

    In contrast to elevation, depression in OCT describes the inward displacement or concavity of retinal structures.  This can be a manifestation of various pathological processes, with a prominent example of degenerative myopia.

    oct scan normal eye

    Degenerative myopia on OCT

     

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    OCT scan: normal eye transformation through pathologies

    Age-related macular degeneration (AMD)

    AMD is an acquired degenerative macular disease usually affecting individuals over the age of 55 years. It is characterized by pathologic alterations of the outer retina, retinal pigment epithelium (RPE), Bruch’s membrane, and choriocapillaris complex, including drusen formation and pigmentary changes.

    AMD is a progressive disease, and in advanced stages, central geographic atrophy and neovascularization, may develop and reduce vision. OCT plays a critical role in distinguishing between the different stages and forms of AMD, particularly when compared to the features of an OCT normal scan.

    Wet AMD

    normal abnormal oct scan

    Neovascular or “wet” age-related macular degeneration (nAMD) arises from the aberrant growth of choroidal vessels that penetrate Bruch’s membrane and invade the subretinal space. These abnormal vessels leak fluid and blood, disrupting the retinal architecture and causing vision loss. 

    Several key OCT features can signal the presence and activity of nAMD in comparison to a normal OCT scan:

    • Fluid Accumulation: The presence and location of fluid are hallmarks of nAMD (hence the term ‘wet AMD’). Intraretinal fluid, appearing within the retinal layers, often signifies more severe disease and a poorer visual prognosis than subretinal fluid, which accumulates beneath the retina.
    • RPE Detachment: Serous PED appears as a dome-shaped elevation of the RPE due to fluid accumulation beneath it. PEDs often accompany nAMD and can vary in size and shape.
    • Disruption of Retinal Layers: nAMD can disrupt the normal retinal architecture, particularly the photoreceptor layer. Damage to the ellipsoid zone (EZ) and external limiting membrane (ELM) is visible on OCT and correlates with visual impairment.
    • Hyperreflective Foci: Hyperreflective dots (HRDs) are small, bright spots scattered throughout the retina.
    • Subretinal Hyperreflective Material (SHRM): Appears as a hyperreflective band between the retina and RPE. Its composition varies but may include fluid, fibrin, blood, and neovascular tissue; it can be associated with poorer visual outcomes.
    • RPE Tears: These are disruptions in the RPE monolayer, often occurring in areas of PED. RPE tears can lead to significant vision loss and are an important complication of nAMD.
    • Choroidal Changes: nAMD can also affect the choroid, the vascular layer beneath the RPE.

    Dry AMD

    normal abnormal oct scan

    In its early stages, Dry AMD is characterized by drusen and pigmentary abnormalities resulting from alterations in the retinal pigment epithelium (RPE). Later, it can progress to geographic atrophy (GA) or outer retinal atrophy.

    The three classic findings in Dry AMD are drusen, pigmentary changes, and geographic atrophy.

    Drusen are classified as:

    • small (<65 um), 
    • medium (65 – 124 um), 
    • or large (>125 um). 

    While both drusen and pigmentary changes can appear as yellowish deposits in the retina, pigmentary changes are often more varied in color (ranging from yellow to brown or black) and less defined in shape than the generally circular drusen.

    Geographic atrophy typically begins in the paracentral macula, often surrounding the fovea in a horseshoe pattern. It can eventually involve the fovea itself, leading to severe vision loss.

    Diabetic Retinopaty (DR)

    normal macula oct

    Diabetic retinopathy (DR), a leading cause of vision loss in working-age populations, is characterized by retinal vascular abnormalities. It progresses from non-proliferative DR (NPDR), marked by vascular leakage and capillary occlusion, to proliferative DR (PDR), where neovascularization can lead to severe vision impairment through vitreous hemorrhage or retinal detachment.

    OCT can aid in identifying the earliest sign of DR: microaneurysms. They appear as small, distinct, oval-shaped, hyperreflective, walled structures associated with microvascular damage. Specifically, the structural weakness of the vessel wall of MAs causes fluid leakage, resulting in edema.

    oct scan normal eye

    Another consequence of microaneurysm formation is the progression to intraretinal hemorrhages (IRH), often called ‘dot-blot’ hemorrhages. These appear as hyperreflective foci on OCT cross-sections, with varying degrees of opacification.

    Diabetic macular edema (DME) can occur at any stage of the disease and is the most common cause of vision loss in those with diabetes. It results from a blood-retinal barrier breakdown, leading to fluid leakage and retinal thickening.

    Retinal vein occlusions

    normal macula oct

    Retinal vein occlusions (RVOs) are blockages of the retinal veins responsible for draining blood from the retina. These blockages can affect either the central retinal vein (CRVO) or one of its branches (BRVO). RVOs are more prevalent in older individuals and those with underlying vascular conditions such as high blood pressure, high cholesterol, a history of heart attack or stroke, diabetes, or glaucoma. The primary vision-threatening complications of RVO are macular edema, which involves fluid accumulation in the central retina, and retinal ischemia, which results from insufficient blood flow to the retina.

    While both Central Retinal Vein Occlusion (CRVO) and Branch Retinal Vein Occlusion (BRVO) involve blockage of a retinal vein, the underlying cause and location of the blockage differ.

    CRVO occurs when a thrombus (blood clot) blocks the central retinal vein near the lamina cribrosa, where the optic nerve exits the eye.

    In contrast, BRVO typically occurs at an arteriovenous crossing point, where a retinal artery and vein intersect. Atherosclerosis (hardening of the arteries) can compress the vein at this crossing point, leading to thrombus formation and occlusion.

    In CRVO, the retina often exhibits extensive intraretinal hemorrhages, dilated and tortuous veins, and cotton-wool spots. This constellation of findings is classically described as a “blood and thunder” appearance. In BRVO, the signs are typically localized to the area of the retina drained by the affected vein. Macular edema, characterized by retinal thickening and cystoid spaces within the retina, is a common finding in CRVO and BRVO and can significantly contribute to vision loss.

    Central serous retinopathy

    normal abnormal oct scan

    Central serous chorioretinopathy (CSCR) is a common retinal disorder that causes visual impairment and altered visual function. It is classified as a pachychoroid disease, including conditions like polypoidal choroidal vasculopathy and pachychoroid neovasculopathy. 

    OCT imaging in CSCR often reveals a thicker-than-average choroid.

    This diagnostic is particularly useful in cases where clinical examination findings are inconclusive, distinguishing subtle differences between normal and abnormal OCT scans in terms of structural changes, such as small pigment epithelial detachments (PEDs) and hyperreflective subretinal fluid, that may not readily appear on clinical exams.

    Furthermore, OCT is valuable for monitoring disease progression and resolution in chronic CSCR cases. A distinguishing feature that can also be seen in CSR is the appearance of the retinal pigment epithelium: the RPE line typically appears straight in non-affected areas, while it can appear wavy or irregular in areas with CSCR.

    Epiretinal membrane (Epiretinal fibrosis) 

    oct scan normal eye

    Epiretinal fibrosis (epiretinal membrane/macular pucker) is a common condition affecting the central retina, specifically the macula. It is characterized by a semi-translucent, avascular membrane that forms on the retinal surface, overlying the internal limiting membrane (ILM), which is absent on a normal macula OCT.

    OCT plays a crucial role in assessing the severity of ERMs, revealing the extent of macular distortion and the involvement of retinal layers.

    OCT findings in ERMs are used to stage the severity of the membrane, ranging:

    • Stage 1: ERMs are mild and thin. Foveal depression is present.
    • Stage 2: ERMs with widening the outer nuclear layer and losing the foveal depression.
    • Stage 3: ERMs with continuous ectopic inner foveal layers crossing the entire foveal area.
    • Stage 4: ERMs are thick with continuous ectopic inner foveal and disrupted retinal layers.

    Retinal detachment

    normal abnormal OCT scan

    Retinal detachment is an important cause of decreased visual acuity and blindness, a common ocular emergency often requiring urgent treatment.

    It occurs when subretinal fluid accumulates between the neurosensory retina and the retinal pigment epithelium through three mechanisms:

    • Rhegmatogenous: a break in the retina allowing liquified vitreous to enter the subretinal space directly.
    • Tractional: proliferative membranes on the surface of the retina or vitreous pull on the neurosensory retina, causing a physical separation between the neurosensory retina and retinal pigment epithelium
    • Exudative: accumulation of subretinal fluid due to inflammatory mediators or exudation of fluid from a mass lesion/insufficient RPE function

    OCT helps identify foveal status and diagnose tractional or exudative retinal detachments, aiding in treatment planning.

    Macular hole

    normal macula oct

    Macular holes are full-thickness defects of retinal tissue involving the anatomic fovea and primarily the foveola of the eye. They are thought to form due to anterior-posterior forces, tangential forces and weakening in the retinal architecture that result in openings in the macular center. 

    The International Vitreomacular Traction Study (IVTS) Group formed a classification scheme of vitreomacular traction and macular holes based on OCT findings:

    • Vitreomacular adhesion (VMA): No distortion of the foveal contour; size of attachment area between hyaloid and retina defined as focal if </= 1500 microns and broad if >1500 microns
    • Vitreomacular traction (VMT): Distortion of foveal contour present or intraretinal structural changes in the absence of a full-thickness macular hole; size of attachment area between hyaloid and retina defined as focal if </= 1500 microns and broad if >1500 microns.
    • Full-thickness macular hole (FTMH): Full-thickness defect from the internal limiting membrane to the retinal pigment epithelium. Described 3 factors: 1) Size – horizontal diameter at narrowest point: small (≤ 250 μm), medium (250-400 μm), large (> 400 μm); 2) Cause –  primary or secondary; 3) Presence of absence of VMT.

    Glaucoma

    oct scan normal eye

    Glaucoma is a progressive optic neuropathy that is multifactorial and degenerative. It is characterized by the death of retinal ganglion cells (RGCs) and their axons, leading to the characteristic optic disc and retinal nerve fiber layer (RNFL) structural changes and associated vision loss. One of the most effective ways to get information about nerve states is OCT.

    The Glaucoma OCT test provides valuable information about ganglion cells: damage to the ganglion cells or their processes leads to thinning across respective layers, which we can measure as the thickness of the ganglion cell complex. 

    Key things to focus on when working with OCT for glaucoma detection:

    • Look for thinning of the pRNFL, particularly in the inferior and superior quadrants, asymmetrical thinning between a patient’s eyes
    • Assess the thickness of the ganglion cell-inner plexiform layer, macular RNFL, and the overall ganglion cell complex. 
    • Monitoring: Seek significant decreases over time in pRNFL thickness (≥5 μm globally, ≥7-8 μm in specific sectors) or in average GCIPL thickness (>4μm).

    AI-powered OCT interpretation tools, such as Altris AI, AI for OCT, can further assist clinicians by providing automated calculations of RNFL thinning in the upper and lower hemispheres and the asymmetry levels between them.

    FDA-cleared AI for OCT analysis

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    Summing up

    OCT has revolutionized ophthalmology, bringing a wealth of new details and challenges. The human eye can easily miss subtle abnormalities on complex scans, making accurate interpretation critical. While experience is essential, relying solely on  “learning by doing” poses risks. 

    AI-powered OCT interpretation software bridges this gap, offering a safety net during the learning curve and beyond. AI-powered second opinion on OCT scans enhances diagnostic accuracy, empowers clinicians, and allows them to spend more time for a meaningful connection with patients.

  • Optometry Practice Growth: Business Cases

    how to grow an optometry practice
    Altris Inc.
    03.10.2024
    8 min read

    Optometry practice growth: business cases

    The client. Dr. William C. Fruchtman’s Optometry Practice, owned and operated by Dr. William C. Fruchtman, O.D., is located in East Rutherford, New Jersey, an inner-ring suburb of New York City. With over 30 years of service to the community, the practice provides comprehensive eye care, including regular eye examinations, contact lenses, and glasses prescriptions. 

    Dr. William Fruchtman’s practice continually seeks opportunities to add value to its services. He is cultivating his expertise in dry eye disease and macular degeneration, implementing advanced technologies, and using another effective strategy to expand his patient base – communicating with patients in their preferred language. Knowing that clear communication is vital to good care, Dr. William C. Fruchtman’s team includes members who speak Spanish and Polish. As such, their website is available in both Polish and Spanish, a valuable asset considering the area’s substantial Spanish-speaking population (up to 20% of the local demographic).

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    While achieving fluency in every language spoken within your community may not be feasible, consider adapting your website and patient materials to include translations in commonly spoken languages. As Dr. Fruchtman’s experience confirms, even a simple greeting in a patient’s native language can create a bond with patients or, at the very least, prompt a genuine surprised smile.

    optometry practice growth

    The problem. To establish expertise in specialized services, Dr. William Fruchtman has been committed to effectively managing dry eye disease and macular degeneration. Not so long ago, the practice implemented Equinox Low-Level Light Therapy (LLLT). This advanced dry eye treatment utilizes LED lights to warm the eyelids gently, promoting meibomian gland function and oil release. With dry eye management addressed, Dr. Fruchtman sought an additional tool to both strengthen his decision-making when managing patients with other pathologies, particularly macular degeneration, and increase his optometry practice growth.

    The solution. After researching Altris AI, an Artificial Intelligence platform for OCT scan analysis, Dr. Fruchtman was positive that he wanted to try the platform. Following introductory meetings and a quick onboarding with the Altris team, he started a two-week trial. After personally testing the platform, Dr. Fruchtman decided it was an invaluable addition to his practice.

    optometry practice growth

    Integrating Altris AI into the practice has notably enhanced Dr. Fruchtman’s confidence and precision in diagnosing and managing eye care disorders. The practice has also gained a significant competitive advantage, as the platform can routinely perform Glaucoma Risk Analysis on existing OCT scans, offering additional value to patients. 

    Thanks to the color-coded and labeled OCTs, optometry facilitates patient education and enables practitioners and patients to monitor the progression or treatment results more effectively. 

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    How to grow an optometry practice: more cases from optometry owners

    Optometrists undergo years of education, training, practice, and continuous learning – understandably, it is hard to see additional time or resources to pursue business education. 

    Many practitioners experience stress, balancing patient care demands with the realities of running a profitable business. This feeling can intensify when attending countless conferences and webinars highlighting thousands of ways to make business more efficient. While they offer valuable advice, it’s sometimes helpful to remember simple points of how successful optometry practice growth will look: attracting new patients, retaining existing ones, and ensuring a smooth and efficient workflow. These (even though overly simplified) points allow you to focus on the most critical details.

    But before diving into ways of optometry practice growth, remember that the first step is a realistic assessment of your current situation. 

    While you’re likely aware of some issues, feedback from your team and patients can provide insights, and sometimes even immediate solutions, for areas of improvement. 

    Even though we cannot directly assist in assessing your specific practice, as you know it best, below we offer some key, proven strategies for growing your business.

    Optometry practice growth: expanding your patient base

    • Dry Eye Specialization

    One effective strategy for optometry practice growth is to expand the scope of services to include the diagnosis and management of ocular diseases. For example, dry eye disease (DED) affects ∼344 million people worldwide and over 20 million in the United States alone, yet many remain undiagnosed and untreated. This presents a significant opportunity to care for a large and often underserved patient population. By developing expertise in DED and offering specialized treatments, you can not only attract new patients but also contribute to improving the quality of life for those suffering from this chronic condition.

    how to grow an optometry practice

    There are numerous approaches to managing DED effectively. As mentioned, Dr. William C. Fruchtman’s practice utilizes Equinox Low-Level Light Therapy (LLLT). 

    Dr. Shane Swatts, O.D., owner of Eastern Virginia Eye Associates, employs AI software to enhance DED diagnostics, conduct more comprehensive analyses, and keep detailed patient medical histories. This technology upgrades pre-and post-operative care, saving time without compromising accuracy.

    how to grow an optometry practice

    • Aesthetic Optometry

    Dr. Janelle Davison identified an opportunity for optometry practice growth by addressing patient needs while generating additional revenue by incorporating aesthetic optometry services into her practice. Within a single quarter, her practice generated $14,000 in revenue from aesthetic product sales alone. 

    how to grow an optometry practice

    Source

    Dr. Davison also collaborates with a licensed aesthetician who operates within the practice on a contract basis, sharing the revenue generated from aesthetic services.

    improve efficiency in optometry office

    • Glaucoma Management

    Dr. James Deom, O.D., M.P.H., an optometrist from Pennsylvania, implemented a successful strategy for optometry practice growth based on attracting glaucoma patients, significantly increasing glaucoma-related revenue. He initiated internal marketing efforts by inquiring about patients’ family history of glaucoma and informing them about the practice’s newest technology for the early detection of vision loss.

    improve efficiency in optometry office

    Practices specializing in glaucoma management can significantly benefit from incorporating advanced software solutions to complement their existing diagnostic hardware. For instance, integrating Altris AI, AI for OCT,  into their OCT analysis workflow enables not only automated screening of 70+ pathologies and biomarkers but includes assessing retinal nerve fiber layer (RNFL) asymmetry for glaucoma risk evaluation.

    • Patient-Centered Care

    Offering diverse channels for patient interaction can broaden your practice’s reach and improve the patient experience. Dr. Melissa Richard, O.D., sought to provide patients with a preview of frame options before their appointments. To achieve this, she integrated Optify technology into her practice, a solution she discovered during a Vision Source Exchange lecture. This technology creates a virtual showroom where patients can explore and select their preferred frames in advance, streamlining the in-office experience.

    optometry practice growth

    Patient education is also key to patient-centered care and personalization, which not only empowers individuals and improves their outcomes but also fosters optometry practice growth. Those who understand their eye health are more likely to adhere to recommendations. 

    A study demonstrates that 94% desire educational content, but a third don’t receive it. 

    Providing color-coded OCT reports with pathologies, biomarkers, and pathology progression tracking not only satisfies this need but also elevates your practice above competitors.

    improve efficiency in optometry office

    Improve efficiency in the optometry office through strategic partnerships & team building

    When optometrists consider further career development, they may seek additional support to achieve their goals. Dr. Linda Enciso, O.D., found such support when her practice joined the AEG Vision family in 2019. The transition brought numerous positive changes, boosting patient care and fostering growth opportunities for team members.

    Although Dr. Enciso had already been operating her practice for 13 years and had implemented electronic health records (EHR) systems and third-party software to improve patient communication and boost optometry practice growth, her goal was to continue these advancements and expand the scope of practice.  Joining AEG Vision allowed her to transition to the training team, access continuing education opportunities to stay informed about advancements in optometry and healthcare, collaborate with other healthcare providers and cross-functional teams to enhance comprehensive patient care.

    optometry practice growth

    While the phrase “team building” might evoke images of complicated activities and extensive effort, fostering a strong team can be achieved through simple, engaging initiatives. Consider the inspiring example of Dr. Jonathan Cargo, O.D.  

    Dr. Cargo recognizes the value of personal development through reading but finds it challenging to share his insights with his team effectively. Inspired by his wife’s long-standing book club, he initiated an office book club to encourage team connection and shared learning to improve efficiency in the optometry office.

    The book club operates with team members suggesting relevant titles and collectively reading chapters over a month, dedicating time during team meetings for discussions. Dr. Cargo highlights the recent success of reading “Crucial Conversations,” a selection prompted by team members’ desire to deepen their communication skills, particularly in navigating challenging discussions with colleagues, patients, and even family members.  The shared reading experience gave a better understanding of effective communication strategies and empowered the team to navigate difficult conversations.

    improve efficiency in optometry office

     

    Summing up

    When regarding optometry practice growth, consider the time, effort, and resources you are prepared to invest. To expand your patient base, explore the addition of new services.

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    To optimize costs and efficiency and gain a competitive edge, investigate the possibility of implementing AI in your practice – it can be a second-opinion tool, or you can read here how practitioners use it for marketing, creating educational materials, and more. To encourage staff retention and nurture a positive work environment, prioritize team-building activities; even seemingly simple initiatives can produce significant benefits.

     

  • Optometry Trends in Action: 12 Real-World Success Stories

    Maria Znamenska
    17.09.2024
    8 min read

    Optometry Trends in Action: 12 Real-World Success Stories

    Optometry trends explained: showcasing real-world optometry practice owners who are adapting to the shift in patient needs, successfully implementing solutions to automate routine and laborious tasks, using AI to combat staff shortages, creating their own brand mascots, and more.

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    Optometry trends for the patient journey: digital communication

    Online shopping, global deliveries, and instant brand replies through messengers have dramatically shifted client expectations and behaviors. The ‘convenience economy’ isn’t slowing down, pushing businesses to adopt technology for more streamlined consumer experiences. 

    What does this mean for your practice? Your patients now expect fast and efficient communication across all touchpoints –  from online scheduling to contactless payments. Transforming your practice to meet these demands ensures satisfied patients and contributes to long-term success, as any optometry practice thrives on the individual experiences of the patients it provides.

    46% of optometrists reported that patient expectations have risen since the pandemic.

    Practices can optimize their workflows in various ways, but generally, the goal is to automate routine administrative tasks, free up staff, and reduce patient waiting time. Digital safety forms and document management systems eliminate physical paperwork, while online proofing and approval systems speed up document processing.

    Optometry trends

    Dr. Justin Bazan, owner and optometrist at Park Slope Eye, New York, has taken this even further by eliminating phone calls at his office entirely and is pleased with the results. This solution was based on several months of analyzing data related to phone calls, including time spent on calls and the frequency of missed calls. The team recognized that while the staff could simultaneously chat with multiple patients, they could only handle one phone call at a time.

    trends in optometry

    Chad Fleming, OD, Owner and OD at Wichita Optometry, Kansas, also identified the need for an enhanced digital presence to prioritize patient convenience. His practice faced the challenge of managing a high volume of phone calls and text messages, requiring either additional staff hiring without an immediate increase in revenue or a strategic reallocation of existing personnel.

    optometry industry trends

    Dr. Fleming optimized the patient experience by setting up automated checkouts at some of his practice locations. This approach enabled him to reassign three front desk employees to the digital communications team. While the transition required patient education to familiarize them with the virtual check-in process on iPads, it did not result in patient attrition.

    optometry industry trends

    Source

    Brianna Rhue, OD, Owner and Optometrist of West Broward Eyecare Associates, Florida, agrees that the traditional approach of answering calls and checking emails once a day differs from today’s patient expectations. She advocates step-by-step optimizations throughout the patient journey to eliminate unnecessary wait times and increase productivity.

    trends in optometry

    Upgrading to a more advanced EHR system is one of the significant opportunities to streamline practice operations, save practitioners time, money, and stress, and align with optometry industry trends. Unfortunately, once hailed as revolutionary, some widely adopted EHR solutions are now criticized for their burdensome workflows and counterintuitive interfaces. This has led some practitioners to describe their interaction with systems as “death by a thousand clicks.”

    By leveraging up-to-date EHR features like customizable patient encounter templates, integrated imaging and diagnostic tools, and patient outcome tracking, eye care professionals can shift their focus from paperwork to patient care.

    Another of optometry trends gaining momentum among optometry practice owners is offering flexible payment options. This reflects not only the growing demand for convenience but also the financial constraints of patients navigating the current economy that is heading to a recession.

    Dr. Rhue encourages practices to adopt mobile payment solutions that enable patients to pay electronically using platforms like Apple Pay, Venmo, or PayPal at the point of service. For balances due after the visit, the ability to send secure payment links via text message can greatly enhance the collection process.

    optometry trends

    Source

    Furthermore, providing patient financing options empowers patients to choose how and when they pay. This offers additional convenience for both parties and eliminates friction by allowing patients to spread the cost of their care over time rather than requiring full payment upfront.

    If you are still determining which technologies of these optometry industry trends your patients will be eager to adopt, consider the approach taken by Scott Jens, OD, the owner of Isthmus Eye Care, Wisconsin. Dr. Jens has successfully implemented post-examination surveys to gather patient feedback. This strategy serves a dual purpose: demonstrating your commitment to patient satisfaction and gaining valuable insights into which technological advancements would most benefit your practice.

    optometry trends

     

    Optometry trends in the exam room: tech-driven precision and patient education

    Optometry relies heavily on technology, and investing in hardware upgrades is a significant financial commitment. However, if your hardware needs are met, but you still want to be at the forefront of technological advancements, consider specialized software and platforms to extend the possibilities of your existing devices.

    Dr. Maria Sampalis, OD, the owner of Sampalis Eye Care, Rhode Island, utilizes two such programs in her practice. To support her specialization in dry eye management, she employs CSI Dry Eye. Additionally, she uses Altris AI, an AI-powered platform for OCT scan analysis, to provide a second opinion and enhance diagnostic accuracy.

    Dr. Sampalis finds that the Dry Eye software allows her and her staff to analyze symptoms and images comprehensively, improving patient care, time savings, and increasing diagnostic precision. See how OCT AI works here. 

    Her patients also appreciate Altris AI, which analyzes OCT scans for over 70 pathologies and biomarkers while also calculating the risk of developing glaucoma.

    optometry industry trends

    Working with specialized software solutions improves diagnostic accuracy and aids in patient education. Visual representations of their conditions, facilitated by these technologies, empower patients with a clearer understanding, leading to increased treatment compliance.

    Optometry trends

    Eye Place, an optometry center in Columbia, also leverages Altris AI, among other cutting-edge technologies. They capture images using the Topcon Maestro2 OCT and use Image Net6 software to export DICOM files to the Altris AI platform.

    trends in optometry

    Beyond AI-powered OCT analysis, Eye Place utilizes state-of-the-art diagnostic tools, such as 3D OCT equipment, to screen for serious conditions, including glaucoma, diabetes, and macular degeneration. Furthermore, they work with AdaptDX Pro, a technology capable of detecting macular degeneration earlier than traditional methods.

    Another case of optimizing and enhancing the exam process is West Broward Eyecare Associates. They implemented  Optify, a smart building solution offering full fiber connectivity. Patients can pre-select frames in the online optical store before their visit, streamlining the in-office experience. Additionally, the practice utilizes Dr. Contact Lens, a platform for convenient ordering, reordering, and prescription management for contact lens wearers, reducing paper waste.

    There are also advancements in AI transcription technology that are poised to ease clinical documentation and automate a traditionally laborious task.

    The adoption of AI in clinical documentation has been shown to reduce the time doctors spend on charting by approximately 2 hours per day. 

    AI exam transcription is still in the process, and the existing possibilities are not yet flawless—struggling with patient responses like “mm-hm” and “uh-huh”—the technology is evolving, promising greater efficiency and accuracy in the future. For example, one such program starts the transcription process of the exam by confirming patient consent and a click of the record button by the optometrist. Then, AI captures, structures, and summarizes information in real-time, filtering for relevant details to generate documentation for each patient appointment. 

    Optometry trends for competitive advantage: using AI in Marketing and Decision-making

    Some practice owners may still believe their patient demographics do not necessitate an expanded online presence, particularly when considering elders. But you should be different from your competitors.

    The reality is that today’s patients, regardless of age, are increasingly turning to the Internet for information and services. While word-of-mouth referrals remain valuable, a solid online presence is essential for practice growth and visibility in today’s competitive landscape.

    Twin Forks Optometry and Vision Therapy in New York reports that their most effective marketing strategy involves a monthly-to-quarterly newsletter distributed to existing patients. This newsletter highlights practice updates, recent vision therapy graduates, new podcast episodes, and seasonal information. They’ve also observed that educational posts generate significant engagement and have even led to new patient visits.

    optometry industry trends

    Voice Search Optimization (VSO) is emerging as one of the new trends in optometry that has the potential to benefit practices significantly. Dr. Brianna Rhue, OD, co-owner of West Broward Eyecare Associates in Florida, asserts that a search engine optimized (SEO) website alone will soon be insufficient for patients to discover your practice online easily, especially in highly competitive locations.

    Contrary to popular belief, it’s not just the tech-savvy individuals who rely on voice assistants. This technology is predominantly used by older individuals who haven’t mastered typing or face difficulties with it.

    However, while the benefits of digital communication are undeniable, it’s crucial to acknowledge that it often adds up yet another layer of responsibility to already overburdened teams. This is why generative AI tools like ChatGPT and Gemini are gaining popularity among optometrists, offering solutions to this and other challenges.

    For example, Dr. Ryan Cazares, the owner and founder of Scott Eye Care in Louisiana, utilizes ChatGPT to generate social media and educational content for his practice. He brainstorms with AI content ideas, creates visuals for social media and marketing campaigns, and has even developed a unique mascot (Dr. Seymour) that engages his audience.

    Trends in optometry

    The practitioner also uses AI to generate personalized educational materials for their patients. Traditionally, his practice relied on generic Optometric Association pamphlets, but now, it has transitioned to simple one-page educational sheets tailored to individual patient needs.

    trends in optometry

    Dr. Haley Perry, owner of Elite Eye Care, New York, provides another example of AI’s potential in practice management. Her goal for this year was to increase patient volume without expanding her staff, and ChatGPT played a pivotal role in achieving this objective. 

    Faced with the decision between two vendors for new exam room equipment, she used AI to analyze each vendor’s pricing and financing options, weigh the pros and cons of the equipment in relation to her goals, and forecast the return on investment (ROI) for each option. This analysis enabled her to select the most suitable vendor and estimate the timeframe for recouping her investment.

    Dr. Perry also leverages AI to analyze patient feedback, demographic data, and treatment outcome statistics to ensure equipment investments align with patient needs. For instance, if data reveals a high prevalence of conditions like glaucoma, AI can help justify investing in advanced glaucoma screening tools.

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    Summing up

    The optometry landscape is evolving, driven by raised patient expectations for convenience and efficiency. Practices adapt to these changes by embracing emerging optometry trends to achieve more precise diagnostics, streamline patient journeys, enhance the exam room experience, and build trust and connection. Much of this technology is AI-based, with even more advancements on the horizon. So, optometrists implementing these solutions today are poised to secure a significant competitive advantage.

     

  • How we build Ethical AI at Altris AI

    Andrey Kuropyatnyk
    03.09.2024
    13 min read

    How we build Ethical AI at Altris AI

    As the co-owner of the AI HealthTech startup, I get many questions regarding biases and the security of our AI algorithm. After all, Altris AI works directly with patients’ data, which is why these questions are inevitable and even expected. So, I decided to share our approach to building Altris AI as an ethical AI system. 

    From the very first moments of the company’s creation, I knew that AI and healthcare were two topics that had to be handled very carefully. That is why we ensured that every aspect of the AI platform creation aligned with modern security and ethics guidelines.

    It’s like building a house: you need to take care of the foundation before getting to the walls, roof, and decor. Without it, everything will fall sooner or later. Ethical principles of AI are this foundation.  

    The following aspects of Ethical AI were the most important for us: machine training ethics, machine accuracy ethics, patient-related ethics, eye care specialists-related ethics, usefulness, usability, and efficiency.

    FDA-cleared AI for OCT analysis

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    1. Machine Training Ethics

    To create an accurate algorithm capable of analyzing OCT scans, we needed to train it for years. When it comes to machine training, we speak about data for this training. There are 2 major aspects of machine training ethics that need to be discussed: data ownership and data protection

    Data ownership/Data privacy indicates authority to control, process, or access data. By default, all patients’ data belongs exclusively to patients; no one owns it and sells it to a third party. For Altris AI machine training, all the data was obtained from patients directly who voluntarily agreed to share it and signed relevant documents.

    More than that, no client’s data, under any circumstances, is used to train the Altris AI.

    Data protection

    • GDPR

    Currently, there are the following regulations to protect the confidentiality of patients’ data. The European Union (EU) has legislatures of General Data Protection Regulation (GDPR), Cybersecurity Directive, and Medical Devices Regulation.

    • HIPAA

    In the US, the Health Insurance Portability and Accountability Act (HIPAA) is suggested as a counterpart for European legislation to cover wider confidentiality issues in medical data.

    At Altris AI, we obtained EU certification and ensured that all data is GDPR and HIPAA-compliant. This also applies to all the patients’ data we receive. 

    • European Union Artificial Intelligence Act

    Provider obligations

    As a provider of a high-risk AI system, we comply with the obligations listed under Article 16.

    High-risk obligations

    Under Article 6, high-risk obligations apply to systems that are considered a ‘safety component’ of the kind listed in Annex I Section A, and to systems that are considered a ‘High-risk AI system’ under Annex III.

    At Altris AI we followed these obligations:

    • Established and implemented risk management processes according to Article 9.
    • Used high-quality training, validation, and testing data according to Article 10.
    • Established documentation and design logging features according to Article 11 and Article 12.
    • Ensured an appropriate level of transparency and provided information to users according to Article 13.
    • Ensured human oversight measures are built into the system and/or implemented by users according to Article 14.
    • Ensured robustness, accuracy, and cybersecurity according to Article 15.
    • Set up a quality management system according to Article 17.

    Transparency Obligations

    At Altris AI we also followed the transparency obligations under Article 50:

    • The AI system, the provider or the user must inform any person exposed to the system in a timely,  clear manner when interacting with an AI system, unless obvious from context.
    • Where appropriate and relevant include information on which functions are AI-enabled, if there is human oversight, who is responsible for decision-making, and what the rights to object and seek redress are.

    2. Machine Accuracy Ethics.

    Data transparency.

    Where transparency in medical AI should be sought?

    Transparency in Data Training:

    1. What data was the model trained on? Including population characteristics and demographics.

    The model’s proprietary training data set was collected from patients from several clinics who consented to share their data anonymously for research purposes. The dataset includes diverse and extensive annotated data from various OCT scanners, encompassing a range of biomarkers and diseases. It does not specifically target or label demographic information, and no population or demographic information was collected.

    2. How was the model trained? Including parameterization and tuning performed.
    The training process for the deep learning model involves several steps:

    • Data Annotation: Medical experts annotated the data, creating the ground truth for biomarker segmentation.
    • Data Preprocessing: The data is augmented using unsupervised techniques (e.g., albumentations library) to increase diversity during training.
    • Model Architecture: The model’s architecture is based on the UNet model with ResNet backbones, incorporating additional training techniques specifically engineered for OCT images.
    • Training Process: The model is trained using supervised learning techniques to predict the output biomarker segmentation mask and diagnosis label, employing backpropagation and gradient descent to minimize the loss function.
    • Parameterization: The model has millions of parameters (weights) adjusted during training. Hyperparameters such as learning rate, batch size, and the number of layers are tuned to optimize performance.
    • Tuning: Hyperparameter tuning is performed using techniques like grid search, random search, or Bayesian optimization to find the optimal set of parameters that improve the model’s performance on validation data.

    3. How has the model been trained to avoid discrimination?
    The model training uses a wide variety of data to ensure exposure to different perspectives, reducing the likelihood of reinforcing a single viewpoint. No data related to race, gender identification, or other sensitive attributes is used at any stage of the model’s lifecycle (training, validation, inference). The model solely requires OCT images without additional markers or information.

    4. How generalizable is the model? Including what validation has been performed and how do you get comfortable that it generalizes well.

    • Validation Methods: The model is validated using a variety of images that were not seen during training.
    • Performance Metrics: Metrics like Dice and F1 score are used to evaluate the model’s performance.
    • Cross-Domain Testing: The model is tested on images from different OCT scanners and time frames to ensure it can generalize well.
    • User Feedback: Real-world usage and feedback help identify areas where the model may not generalize well, allowing for continuous improvement.

    5. How explainable is the model? Including what explainability testing has been done, if any.

    Explainability Techniques: Techniques like SHAP (SHapley Additive exPlanations), GradCAM, and activation visualization are used to understand which parts of the input images the model focuses on when making predictions.

    Medical Expert Testing: Regular testing and analysis are conducted to ensure that the model’s detections make sense to medical experts and that the model’s decisions align with logical and reasonable patterns.

    Any AI system is opaque (unintelligible) for two reasons:

    • Innate complexity of the system itself.
    • Intentional proprietary design for the sake of secrecy and proprietary interests.

    Biases. In most instances, an AI tool that gives a wrong decision usually reflects biases inherent in the training data. Biases might be racial, ethnic, genetic, regional, or gender-based. 

    There should not be any bias related to race and ethnicity because there is no evidence that biomarkers and pathologies manifest themselves differently in patients of different races and ethnicities. Altris AI uses sufficiently diverse gender and age-related data to provide accurate results for OCT analysis.

    3. Patient-related ethics.

    Patient-related ethics in AI are based on the rights of beneficence, nonmaleficence (safety), autonomy, and justice. Patients exercise their rights either explicitly through informed consent or implicitly through norms of confidentiality or regulatory protections.

    Informed Consent. 

    Informed consent is based on the principle of autonomy. It could authorize the partial or complete role of algorithms in health care services and detail the process of reaching diagnostic or therapeutic decisions by machines. Clinicians should explain the details of these processes to their patients. Patients should have the choice to opt in or out of allowing their data to be handled, processed, and shared.

    As these rights can be enabled by eye care professionals, they remain on the side of eye care professionals in our case. However, eye care professionals who use Altris AI not only inform patients about using AI for OCT scan analysis but also use the system to educate patients with the help of color coding. 

    Confidentiality.

    Patients’ confidentiality is a legal obligation and a code of conduct. Confidentiality involves the responsibility of those entrusted to handle and protect patient’s data.

    All the data that is used inside the Altris AI platform is anonymized and tokenized, and only eye care professionals who work with patients see any personal information. For the Altris AI team, this data is viewed as a programming code.

    4. Eye care specialist-related ethics.

    AI systems, like Altris AI, are unable to work 100% autonomously, and therefore, eye care specialists who use them should also make ethical decisions when working with AI. 

    Overreliance on AI. One of the important aspects of physician-related ethics is overreliance on AI during diagnostic decisions. We never cease to repeat that Altris AI is not a diagnostic tool in any sense; it is a decision-making support tool. The final decision will always be made by an eye care professional. It is an eye care professional who must take into consideration the patient’s clinical history, the results of other diagnostic procedures, lab test results, concomitant diseases, and conclusions from the dialogue with the patient to make the final decision. 

    Substitution of Doctors’ Role. Considering the information mentioned above, it is important to clarify the aspect of substituting eye care specialists. It should always be kept in mind that the aim of adopting AI is to augment and assist doctors, not to replace them.

    Empathy. Empathetic skills and knowledge need to be further incorporated into medical education and training programs. AI performing some tasks offers space for doctors to utilize empathy in medical education and training.

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    5. Usefulness, Usability, and Efficacy. 

    According to the Coalition for Health AI (CHAI) checklist, AI in healthcare must be, first of all, useful, usable, and efficient.

    To be useful, an AI solution must provide a specific benefit to patients and/or healthcare delivery and prove to be not only valid and reliable but also usable and effective. The benefit of an AI solution can be measured based on its effectiveness in achieving intended outcomes and its impact on overall health resulting from both intended and potentially unintended uses. An assessment of benefits should consider the balance between positive effects and adverse effects or risks. 

    In the case of Altris AI, its usefulness is proved by the clients’ testimonials we receive regularly. 

    Relatedly, an effective AI solution can be shown to achieve the intended improvement in health compared to existing standards of care, or it can improve existing workflows and processes.

    With Altris AI, we make patient screening and triage faster and more effective. We also significantly improve the detection of early pathologies, such as early glaucoma, which are often invisible to the human eye. 

    Usability presupposes that the AI tool must be easy for healthcare practitioners. Altris AI is actively used by more than 500 eye care businesses worldwide, proving its usability. Moreover, we constantly collect feedback from users and improve the platform’s UI/UX.

    Conclusion

    In conclusion, Altris AI has built its platform with a strong commitment to ethical AI principles, ensuring patient data protection, transparency, and compliance with global regulations like GDPR HIPAA, EU AI Act. The system is designed to support, not replace, eye care professionals by enhancing diagnostic accuracy and improving early detection of diseases. By emphasizing machine training ethics, patient-related rights, and the usability of their AI tool, Altris AI fosters trust in healthcare technology while maintaining high standards of transparency, accountability, and human oversight in medical decision-making.

  • Optometry Technology: What to Expect? 

    optometry technology
    Maria Znamenska
    7 min.
    7 min.

    Optometry Technology: What to Expect? 

    For this article, we surveyed eye care professionals on which optometry technology appears most promising to them. The answers were divided among AI for more precise diagnostics, advanced contact lenses, and new iterations of OCTs.

    Of course, this is not the whole list of possible new tech in optometry, but these are the topics that draw the most attention today. 

    The article delves deeper into each of these technologies, as well as explores oculomics, the new way of understanding the correlation between eye pathology and overall human health.

    Explore how AI for OCT scan analysis really works

    New tech in optometry: AI for Medical Image Analysis

    AI has blossomed in recent years, transforming not only how we work and relax but also how we manage our health. It’s no surprise that our survey of professionals revealed AI as the most promising technology in optometry.

    The most immediate and practical AI implementation in optometry is the analysis of medical images, such as fundus photos and OCT scans.

    They require no additional equipment beyond the OCT and fundus cameras many practitioners already own, are cost-effective, and add huge value to a practice. 

    optometry technology

    There are many companies that detect a number of biomarkers and help with diagnostic decision-making already, and their number will only increase from year to year for several reasons:

    • AI systems for medical image analysis speed up patient triage
    • AI systems help to detect early, minor, and rare pathologies which sometimes can be missed
    • AI systems help with complex cases when a second opinion is needed
    • Quantitative analysis of biomarkers improves treatment results monitoring making it more efficient

    For instance, AI today can assess the early risk of glaucoma based on the GCC asymmetry measurements. Here is how AI-powered OCT workflow would look. 

    AI-assisted readings of OCT scans are already helping not only with pathology detection but also with the analysis of its progression or response to treatment. This represents a new approach to monitoring, where practitioners no longer need to sift through various patient notes but can directly compare reports from previous examinations and observe how, for instance, shadowing has changed in micrometers.

    technology in optometry

    AI programs are becoming even more invaluable with an aging population, as diseases prevalent in older individuals become increasingly common while ophthalmology and optometry face a shortage of specialists. This situation will transform the optometrist’s role, with AI empowering practitioners with the diagnostic capabilities to manage many conditions without referral. This will benefit patients, enabling timely routine screenings and diagnoses and preventing months-long waits that can sometimes lead to irreversible blindness.

    AI systems are also being implemented in ophthalmic trials for biomarker detection, exploring the relationship between imaging biomarkers and underlying disease pathways. For instance, a recent study linked levels of various cytokines, including VEGF, MCP-1, and IL-6, with specific OCT-derived biomarkers like fluid parameters and outer retinal integrity. 

     

    new tech in optometry

    This significantly accelerates the research process, assisting in identifying the right target audience based on OCT scans, eliminating manual data annotation, and revealing the subtlest changes, progression or regression, and patient responses during trials. 

    While material advancements allow us to build more precise machines, the new tech in optometry likely won’t involve some unheard-of device. Instead, AI software will enable us to extract the maximum potential from the technologies we already use.

    Explore how AI for OCT scan analysis really works

     

    New Tech in Optometry: New Iterations of OCT

    Even though OCTs entered the market relatively recently, they swiftly became indispensable ancillary tests in ophthalmic practice for many professionals. The primary reason is their high-quality imaging of the retina, nerve fiber layer, and optic nerve, offering a near in-vivo “optical biopsy” of the retina.

    However, the technology continues to evolve – partly due to technological advancements and partly due to the ability to extract even more data from OCT machines through sophisticated software.

    SD-OCT is undergoing continuous development, expanding its range of applications. Multimodal imaging, which combines SD-OCT with other imaging techniques like autofluorescence and angiography, now allows for improved diagnosis and management of a wider array of diseases. 

    Several prominent OCT evolutions combine technological advancements and promise widespread adoption. They are:

     

    New Tech in Optometry: En-face OCT

    En-face OCT in current systems is based on software reconstruction of OCT images. Image slices are selected retrospectively from full recorded volumes or calculated by depth projection along specific depth ranges, enabling three-dimensional data visualization in a fundus projection. This technique allows the projection of specific retinal and/or choroidal layers at a given depth onto an en-face view.

    new tech in optomery

    While we are more accustomed to working with cross-sectional images (B-scans), microstructural changes and the retinal and choroidal vasculature morphology are challenging to evaluate using B-scans alone. En-face OCT offers numerous advantages, including the ability to precisely localize lesions within specific subretinal layers using their axial location on OCT cross-sections and to register projected OCT images to other fundus imaging modalities using retinal vessels as landmarks.   

    Currently, en-face OCT is being applied to various specialized areas within the eye, encompassing the anterior segment, glaucoma, infectious diseases, and the retina.

     

    Optometry Technology: SS-OCT

    Like SD-OCT, swept-source OCT (SS-OCT) utilizes Fourier domain technology to optimize higher-quality wavelength transduction within the frequency domain. This enables rapid sweeping scan patterns across a broad bandwidth.

    However, instead of a broad-bandwidth light source projected all at once, as in SD-OCT, SS-OCT employs a single tunable laser that sweeps through different frequencies to cover the entire spectrum swiftly. The light reflected from the eye is captured by a photodetector significantly faster than the charge-coupled device (CCD) camera used in SD-OCTs. This difference translates to a faster scanning speed of up to 400,000 axial scans per second, eliminating the typical depth-dependent signal drop-off associated with SD-OCT. Additionally, the faster scanning speed reduces image distortions caused by eye movements and allows for wider B-scans, facilitating widefield imaging.

    Furthermore, many SS-OCT systems utilize a light source centered at an approximately 1050 nm wavelength, providing better tissue penetration than SD-OCT. This allows for visualization of structures like the choroid, lamina cribrosa, and structures at the anterior chamber angle. This enhanced penetration is crucial in diseases like Central Serous Chorioretinopathy, where evaluating the entire thickness of the choroid can be challenging.

    Moreover, volumetric analysis of the choroid and various pathological features can aid in monitoring the progression of Wet AMD, CSCR, and Diabetic Retinopathy, as well as assessing the response to treatments such as anti-VEGF agents, laser photocoagulation, and photodynamic therapy (PDT).

     

    Optometry Trends: OCT Angiography

    Given that many ocular diseases are associated with vascular abnormalities, the ability to visualize and quantify blood flow in the eye is crucial. Traditionally, fluorescein angiography (FA) and indocyanine green angiography (ICGA) have been used for this purpose, but these procedures require intravenous injection of contrast agents, which is not only time-consuming but may lead to allergic reactions or potentially serious side effects.   

    OCTA, on the other hand, produces high-resolution, 3D angiograms of the retinal and choroidal vascular networks, taking advantage of the eye’s unique characteristic as the only organ allowing noninvasive, direct observation of its blood vessels’ structure and function. OCTA detects blood flow using intrinsic signals to capture the location of blood vessels. While it has limitations such as insensitivity to leakage and a relatively small field of view, the development of OCTA has the potential to significantly enhance our understanding of the eye’s physiology and pathophysiology, providing depth-resolved angiographic maps of the tissue’s vascular structure down to the capillary level.

    OCTA is particularly valuable in clinical settings where pathologies like diabetic retinopathy, age-related macular degeneration, retinal vein occlusions, and macular telangiectasia are frequently encountered. These conditions often alter blood flow or the blood vessels themselves in the retina, making imaging these vessels essential for diagnosis and management.

    Wide-Field and Ultrawide-Field OCT (WF-OCT and UWF-OCT)

    While OCT is a powerful ocular imaging tool, it has traditionally been limited by a relatively narrow field of view (FOV) – typically around 20 degrees × 20 degrees. To address this limitation, two advancements have emerged:

    • Wide-field OCT (WF-OCT) with an FOV of approximately 60-100 degrees captures the retina’s mid-periphery up to the posterior edge of the vortex vein ampulla.
    • Ultrawide-field OCT (UWF-OCT) with an FOV of up to 200 degrees, mapping the far periphery of the retina, including the anterior edge of the vortex vein ampulla and beyond.

    WF-OCT provides additional information compared to routine 6-9 mm scans in conditions such as diabetic retinopathy (DR), central serous chorioretinopathy (CSCR), polypoidal choroidal vasculopathy (PCV), peripapillary choroidal neovascular membrane (CNVM), or uveitic entities. It facilitates easier visualization of anatomical details of peripheral retinal changes like ischemic areas in DR, retinal vein occlusions, or sites of retinal breaks, peripheral retinal detachment, retinoschisis, and choroidal lesions (melanoma, nevus, hemangioma, choroidal metastasis).   

    As with other OCT iterations, WF and UWF OCT will likely provide the most significant insights when routinely combined with other modalities, such as OCT angiography.

    optometry technology

     

    New Tech in Optometry: Advanced contact lenses

    In our lifetime, contact lenses have evolved from mere corrective devices to sophisticated optical instruments. There are several ways that contact lenses (CLs) continue to advance:

    • Manufacturing optimization: Automation and robotization of the process for higher precision and a shift towards a more environmentally friendly approach.
    • Design: More precise designs tailored to the wearer’s eye with the help of 3D printing.
    • Material advancements: Nanotechnology/surface modifications for improved wettability, lubricity, and antimicrobial properties. Increased focus on biomimetic design.
    • Technological advancements: Smart lenses with thin and ultra-thin transistors capable of reacting to or registering the wearer’s stress levels, glucose levels, etc.

    Let’s take a closer look at a few examples of Smart Contact Lenses (SCLs) that combine some of the characteristics mentioned earlier.

    SCLs are wearable ophthalmic devices that offer functions beyond vision correction. These devices are integrated with sensors, wireless communication components, and microprocessors to measure biological markers. They can treat ocular pathologies by delivering drugs, light, heat, and electrical stimulation, or they can aid in diagnosing. Currently, some SCLs can help manage glaucoma, cataracts, dry eye syndrome, eye infections, and inflammation. In development are lenses to treat age-related macular degeneration (AMD), diabetic retinopathy (DR), retinitis, and posterior uveitis. An artificial retina (retinal prosthesis) is in its early developmental stage, with the potential to restore vision to some degree for specific types of blindness caused by degenerative diseases.

    Scientists from the School of Medical Sciences in New South Wales have implanted epithelial stem cells (ESCs) from a healthy eye into a contact lens. This innovation has shown promise in repairing vision loss caused by a damaged cornea. In another breakthrough, scientists from Oregon State University have utilized ultra-thin transistor technology to design SCLs that can monitor the wearer’s physiological state. While this futuristic contact lens is still in the prototype phase, several biotech companies have already expressed interest in its development.

    Smart lenses also show great promise in drug delivery. One of the main challenges with eye drops is their low bioavailability (less than 5%), primarily due to high tear turnover rates, blinking, nasolacrimal drainage, non-productive absorption by the conjunctiva, and the cornea’s low permeability. Therefore, improving bioavailability by increasing the drug’s residence time on the ocular surface remains a critical research focus. 

    Additionally, drug delivery via SCLs can offer more precise dosing. With traditional eye drops, dosage accuracy relies on the patient’s ability to tilt their head and squeeze the inverted bottle correctly, leading to inconsistent application. Consequently, compliance rates for eye drops are low. In contrast, the drug delivery process with SCLs involves lenses loaded with medication for a day or several days, potentially enhancing compliance, especially for individuals accustomed to wearing contact lenses as part of their routine.

     

    optometry technology

    Just as artificial intelligence is merging with ophthalmic devices for detection and analysis, opening new possibilities, optometry trends are also venturing contact lenses into the multidisciplinary field of theranostics, which combines therapeutics and diagnostics. This field is uncovering new avenues of research, shedding light on disease mechanisms, and driving drug and medical device development. Theranostics leverages knowledge and techniques from nanotechnology, molecular and nuclear medicine, and pharmacogenetics to achieve goals such as in vitro diagnostics and prognostics, in vivo molecular imaging and therapy, and targeted drug delivery. This approach is shifting patient care towards proactive strategies and predictive treatments.

    Optometry Technology: Oculomics

    For decades, researchers have sought to measure retinal changes to identify ocular biomarkers for systemic diseases, a field now known as oculomics.

    As mentioned earlier, the eye provides a unique opportunity for direct, in vivo, and often non-invasive visualization of the neurosensory and microvascular systems:

    • The eye shares a common embryological origin with the brain, and the neurosensory retina and optic nerve are considered extensions of the brain, allowing direct observation of the nervous system.
    • Due to the length and continuity of the visual pathway, along with trans-synaptic degeneration mechanisms, damage to the central nervous system often manifests as changes in the inner retina.
    • The blood-retina barrier, similar to the blood-brain barrier, selectively allows the transport of essential substances to these metabolically active structures.
    • The aqueous and vitreous humors are plasma-derived and transport lipid-soluble substances through diffusion and water-soluble substances through ultrafiltration.
    • The lens, which grows continuously throughout life, accumulates molecules over time, providing a potential map of an individual’s molecular history.

     

    The link between the eye and overall human health is not new. However, with the increasing availability and complexity of large, multimodal ocular image datasets, artificial intelligence-based ocular image analysis shows great promise as a noninvasive tool for predicting various systemic diseases. This is achieved by evaluating risk factors, retinal features, and biomarkers. Thanks to the massive datasets generated through recent ophthalmic imaging, which are now being used for deep learning and AI training, oculomics is starting to yield more precise answers. For example, the NHS alone has been conducting eye tests for over 60 years, resulting in databases containing millions of images, complete with patient records and long-term health outcomes. These datasets have been fed into AI algorithms, leading to models that can already predict cardiovascular risk factors with accuracy comparable to the current state-of-the-art methods.

    It’s a significant opportunity because, with the aging population, a primary healthcare focus will be not only extending lifespan longevity but also maintaining crucial healthspan functions. The primary obstacles to both longevity and healthspan are chronic diseases, referred to as the “Four Horsemen of Chronic Disease” (Cardiovascular disease, Cancer, Neurodegenerative disease, and Metabolic disease). Many of these can be, if not entirely prevented, at least minimized in terms of progression through timely detection and intervention.

    One major advantage of discovering biomarkers that can predict diseases is that eye screenings are generally less intimidating than other procedures. For example, a person might regularly visit an optometrist for prescription glasses but avoid routine cervical screenings. A less anxiety-provoking and familiar procedure could significantly impact healthcare engagement. Such screenings could also make a substantial difference for chronic conditions like dementia, diabetes, and cardiovascular disease, which constitute a significant portion of the “burden of disease.”

    Explore how AI for OCT scan analysis really works

    Summing up

    Artificial intelligence has already significantly impacted our lives. It holds immense promise in optometry technology, as its primary capability—analyzing massive datasets—aligns perfectly with eye care, where thousands of images are generated daily. Training on such vast amounts of data will lead to breakthroughs in pathology and biomarker detection and their correlation with overall human health. It will enable us to take a giant leap towards proactive and predictive medicine, helping our patients live longer, healthier lives.

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  • Optometry Practice Management Tips 10 Real Cases for Revenue Increase

    Optometry Practice Management Tips: 10 Real Cases for Revenue Increase

    Maria Martynova
    14.02.2023
    6 min read

    Optometry Practice Management: Tips and Real Cases

    You’re a skilled optometrist, passionate about patient care. But are you prepared for the challenges of running your own practice? Successfully navigating the business side of optometry practice management demands more than just clinical expertise but also a deep understanding of business management principles. This transition involves constant decision-making, from choosing the right location and equipment to hiring and managing staff.

    We’ve gathered information on ten optometry centers that managed to survive the competition and increase their revenue, as well as optometry practice management tips. The articles will guide you through major challenges that many optometry businesses face, such as the retention of specialists, competition with large chains and retailers, and marketing and sales, identifying growth opportunities.

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    How to improve optometry practice: start with the retention of employees

    This problem is vital considering the huge lack of optometry specialists worldwide. According to WHO, 14 million optometrists are needed globally when there are only 331K available. There are several strategies that optometry businesses can use to retain optometrists.

    • Delegating more examinations to technicians

    Eric Rettig, OD, a partner with Mountain View Eye, a Vision Source practice in Pennsylvania, shares their optometry practice management optimization: assigning two technicians to each of its four doctors to delegate the examination process to the technicians. Tasks such as pupil testing, versions (EOMs), and dilation were incorporated into the pretesting protocols. Technicians were also authorized to perform additional testing based on patients’ past diagnoses or complaints. This approach ensured that the doctor had all pertinent data readily available upon entering the exam room, minimizing patient wait times and maximizing efficiency.

    Owners have implemented the change to increase the number of patients seen per hour, but it has also given additional benefit: now doctors can spend more quality time with each patient.

    optometry practice management

    With an average revenue of $400 per patient and 6-7 patient care hours per day in a five-day week, this equated to $2,600 additional revenue per full-time doctor.

    • Using Artificial Intelligence for retina scan analysis.

    Many optometrists find OCT scan analysis challenging and are not confident about their interpretation skills. Using Artificial Intelligence for automated OCT scan analysis can make the work of optometrists more efficient, increasing the number of patients who undergo OCT examination and subsequently increasing revenue.

    One such case is the practice of Dr. William C. Fruchtman’s Optometry, owned and operated by Dr. William C. Fruchtman, O.D., in New Jersey.

    His practice offers comprehensive eye care services, including eye examinations, contact lenses, and glasses prescriptions. Dr. Fruchtman sought a tool to enhance both his optometry practice management and decision-making process in complex cases. His research led him to select Altris AI, an artificial intelligence platform for OCT scan analysis.

    eye care practice management

    Implementing Altris AI has significantly increased Dr. Fruchtman’s confidence and precision in diagnosing and managing eye conditions. The platform has also provided his practice with a competitive advantage. Altris AI features a referral urgency score ranging from green (no need to refer) to red (urgent referral needed). This scoring system helps optometrists avoid both over-referral and under-referral of patients.

    Thanks to the color-coded and labeled OCTs, optometry facilitates patient education and enables practitioners and patients to monitor the progression or treatment results more effectively.

    Biomarkers detected by Altris AI on OCT

    Optometrist Marketing: digital communication trends

    • Concentrating on eyewear sales.

    Jennifer Stewart, O.D., Optometrist and Founder at Look New Canaan, Connecticut, claims that 2 simple optometry practice management techniques can add $75,000 to the annual revenue of any optometry center.  Even more intriguing is that it’s done without seeing additional patients.

    One of these techniques is decreasing the sales of patients’ own frames (POF) glasses. Jennifer Stewart discusses the benefits available to the patient through their managed care plan, emphasizing that if lenses are cut for their own frame and the frame breaks, they will have already used their lens benefit.

    optometry marketing

    The optometrist explains how the patient’s current pair can serve as a backup and then escorts them to the optical area to meet with the optician. Before leaving the patient with the optician, the optometrist speaks privately with the optician, informing them of the patient’s desire to use their own frame and the discussion about the frame’s condition. The opticians have been trained to reiterate this message to the patient.

    The second one is communicating the need for all types of lenses (for computers, reading, and sunglasses), which can be a very effective revenue-generation optometry practice management tip many owners neglect. The optometrist states that these few extra minutes to talk about options available to patients can result in multiple payoffs in optical. This is one of the optometry practice management tips that works for any center.

    • Providing exquisite luxury experience.

    Fabio Pineda, the owner of Eye Boutique in Houston, Texas, previously held a volume-based, medical-style practice with an average per-patient purchase of one frame per year, 5 percent sunglass sales, and an average per-patient revenue of $300-$350. In 2021, the optometrist changed his approach, opening a fashionable boutique-style practice. He shifted to a low-volume VIP clientele and a red-carpet approach with gourmet beverages, pastries, and a dedicated sunglass section with a wide selection.

    This shift in eye care practice management has brought Dr. Pineda unique customers who specifically seek designer glasses and buy 5-9 pairs at a time, spending upwards of $4,000-$7,000 on purchases.

    optometry marketing

    • Using social media and digital marketing tools extensively.

    Your clients spend time on Instagram, Facebook, and Google, so these are the most effective digital marketing channels for communication with potential customers. 

    For instance, as an optometry marketing strategy to engage current and potential patients on social media, Dr. Arian Fartash, optometrist, CEO at GlamBaby, California, and blogger, considers three types of posts:

    • interactive posts that pose questions about product preferences, like showcasing two frames and asking followers which they prefer, encourage audience participation;
    • educational posts featuring interesting eye facts or eye-catching images related to eye health that offer informative content;
    • patient-focused posts showcasing satisfied patients wearing new eyewear to humanize the practice and demonstrate the positive impact of its services.

    eye care practice management

    • Providing a small warranty on all products.

    An optometrist and the owner of Brilliant Eyes Vision Center in Georgia, Janelle Davison, O.D., has implemented an extended warranty program for eyewear purchases to enhance patient confidence and increase revenue. For a $29.99 enrollment fee, patients receive significant discounts on replacement frames and lenses, paying only $50 for each, regardless of the original cost. This warranty, built into most eyewear packages, has proven popular with patients and generated an additional $14,000 in 2021. Dr. Davison has used the popular concept of buying technological equipment with a warranty, like smartphones or computers, that is familiar to customers.

    how to improve optometry practice

    • Educating patients

    According to Wolters Kluwer Health, patients crave educational materials from their providers, yet only two-thirds get them. This leaves patients searching for information, potentially exposing them to unreliable sources.

    Knowing that providing clear, accessible patient education is crucial for understanding and treatment adherence, The Eye Place, optometry from Ohio, is utilizing the full power of AI for OCT analysis tool, Altris AI. Their winning optometry practice management strategy combines decision-making help from the platform and a way to enhance patient education.

    eye care practice management

    Visual representations of patients’ conditions, facilitated by this technology, empower patients with a clearer understanding, leading to increased treatment compliance.

    Eye Care Practice Management: competition with larger chains

    Private offices find it hard to compete with chains like Specsavers in terms of prices or the speed of service. Chains often have better locations and can spend much more money on marketing. So, how to improve optometry practice to win this competition among corporations?  There are several things that big companies don’t have:

    • Offering personalized service and building a relationship with patients. Building a local presence is the key. Your optometrist center can be known and valued if you really care about the community, know each of your clients personally, and understand their pains and needs. More than that, 97% of marketers witnessed a rise in business outcomes as a result of personalization, according to Salesforce.
    • Providing unique, high-quality products unavailable at chain stores is also a worthy opportunity for a small but flexible business. For instance, some optometry centers build their presence relying on rare glasses brands with sophisticated designs. The global therapeutic contact lenses market is expected to grow at a CAGR of 4.90% from 2021-2027, and designer brands will play a crucial role in this growth.
    • Providing exceptional customer service and after-care. Communication with customers is the core of relationships in any sphere, and healthcare is no exception.

    Today it’s easier to communicate with customers using social media, messengers, and telemedicine. This is the one of optometry management tips that not only allows optometry centers to take care of their clients not only during visits but afterward as well are much more profitable.

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    • Storing all the patients’ data effectively and securely is the key to fast and reliable services inside the optometry centers. There are various EHR systems for optometry centers, and finding the best optometry practice software is hard. However, it is always wise to rely on testimonials. Here, you can find another portion of optometry practice management tips that focus solely on the best optometric practice management software with Acuitas activEHR 2.0, MedFlow EHR, Liquid EHR, EyePegasusEHR, Eye Cloud Pro, OD Link, ManagementPlus, Medesk named the best optometric practice management software according to our research and reviews.

    PATIENTS’ NO SHOWS

    A patient no-show is a painful problem for the majority of optometry centers. Patients ignore yearly checkups and forget about follow-up visits whenever they feel better.

    Virtual check-ins increased profitability and reduced the cost of goods sold (COGS) for the partner at Wichita Optometry. Dr. Chad Fleming adopted this optometry practice management approach through the efficient check-in process he observed at Walmart. His practice faced the challenge of managing a high volume of phone calls and text messages, requiring either additional staff hiring without an immediate increase in revenue or a strategic reallocation of existing personnel.

    optometry practice management

    Using software to remind about future visits can be the solution. For instance, Weave software helped Serenity i Care optometry to reduce the number of no-shows up to 30% from 75%. This software automatically informs clients about future visits via e-mails and texts.

    how to improve optometry practice

    There is no need for a team to have endless calls that are not responded to. Demandforce, Solutionreach, and Simplifeye are other solutions that might work, and they can be great software for reminding patients about visits. This is the most well-recommended optometry practice management software to deal with forgetfulness.

    By using these optometry management practice tips and continuously seeking ways to improve patient engagement, streamline operations, and increase efficiency, optometry can increase its revenue and sustain long-term success.

     

  • optometry practice management software

    Optometry Practice Management Software: Top 8 Applications

    Mark Braddon
    13.02.2023
    9 min read

     

    Optometry practice management software is designed for eye care specialists to manage their practices more efficiently and effectively. The software can automate a wide range of administrative tasks, making it easier for practitioners to focus on patient care.

    Unlike other medical practices, optometry involves the management of a much larger number of optical instruments, processes and aids. Therefore, software for optometrists is more complex and multifunctional. It usually includes features such as appointment scheduling, patient registration, billing and insurance claims processing, patient data management, and secure messaging and email communication. The software can also integrate with other technologies, such as electronic health records (EHRs), OCT image management systems and diagnostic equipment.

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    By streamlining administrative tasks and providing practitioners with patient data, optometry practice management software can help eye care clinics improve their operations, increase efficiency, and provide better patient care. The software can be customized to fit the specific needs of individual practices and is often offered on a subscription basis, making it an affordable and accessible solution for eye care clinics of all sizes.

    In this article, we will highlight the main benefits of practice management optometry soft, and provide you with a list of the Top 8 software to look at.

    What are the benefits of practice management optometry software?

    Optometry practice management software can help doctors in multiple ways besides increasing their revenue, efficiency, and productivity. Some of the key benefits of optometry practice management software include the following items.

    optometry practice management software

    • Improved patient management. The software can store and organize patient data, including medical history, examination results, fundus or OCT images, and treatment plans. This information can be easily accessed by practitioners and used to inform patient care.
    • Efficient appointment scheduling. The software can automate appointment scheduling, which can help to reduce the risk of double-booking and minimize wait times for patients.
    • Accurate billing and insurance claims. The software can help to ensure that billing and insurance claims are processed accurately and efficiently, reducing the risk of errors and delays.
    • Increased revenue. By streamlining billing and insurance claims processes, optometry practice management software can help eye care clinics to reduce errors and increase revenue.
    • Easy access to patient records. The software can store and organize patient records, including OCT images, making it easy for doctors to access the information they need to provide the best care possible.
    • Improved patient communication. Some optometry practice management software includes features that allow for secure messaging and email communication between patients and practitioners, making it easier to communicate outside of office visits. 
    • Increased productivity. By automating repetitive tasks, such as appointment scheduling and billing, optometry practice management software can free up time for eye care practitioners to focus on providing an individual approach to each patient.
    • Better patient outcomes. With access to patient data and treatment history, eye care practitioners can provide more informed and effective care. This can lead to better patient outcomes and increased patient satisfaction.

    Overall, optometry practice management software can help eye care clinics to provide better patient care, increase efficiency and productivity, and improve their bottom line. Now let’s take a look at best optometry software.

    Altris AI

    optometry practice management software

    Altris AI is an image management system based on artificial intelligence (AI) tools that assists eye care specialists in OCT scan analysis and interpretation. The solution was designed in cooperation with retina experts to help practitioners detect the pathology from the OCT scan. Altris AI also can be easily integrated with EHR systems or used standalone as a web application.

    To create an Altris AI system, our specialists colored thousands of OCT scans and named more than 100 retinal pathologies and pathological signs to train an AI algorithm. May sound complicated, but the workflow of the image management system is pretty simple.

    1. First, a user uploads an OCT b-scans to the platform, and the AI model evaluates the scans. 
    2. After that, the model differentiates between normal scans and scans with moderate and severe pathology.
    3. With the help of the second step, eye care specialists are able to focus only on serious (red) scans, saving their precious time.
    4. After that, a user can highlight pathological signs with different colors, sort scans by severity level, and zoom.

    It is important to mention that the patient’s diagnosis is always on the eye care practitioner’s side. Altris AI is a tool that provides assistance in support in decision-making and allows its users to see a broader perspective of a patient’s eye health. 

    Watch a short overview of how Altris AI assists eye care specialists with OCT diagnosis.

    In addition, with Altris AI, users can work with all modern OCT equipment and popular data storage formats, such as DICOM of various lengths, png, and jpg. The patient data at all stages is tokenized and protected from disclosure. Eye care specialists can also actively use the Smart Reports feature, which allows users to select a single element (scan, layers, both eyes, etc.) that they want to see in their OCT report.

    Acuitas activEHR 2.0

    best optometry software

    In case you are working at or owning a midsize or large optometry practice, this hybrid electronic health record solution will be quite useful. Acuitas activEHR 2.0 can be hosted in the cloud as well as deployed on-premise, depending on your preferences. This software offers its users a wide range of tools, including electronic medical records, billing software, scheduling, PACs, accounting software and billing services. 

    What is more, Acuitas activEHR 2.0 can provide optometry clinics with various marketing and upselling features, and you can also customize BI reporting and track benefits. Healthcare providers can reach out to patients via either SMS or email, which makes it much easier to schedule an appointment.

    In addition, the optometry practice management software supports such features as IDA (Immediate Data Access), which allows practitioners to automatically update the frames. Acuitas activEHR 2.0 also offers a variety of application integrations. 

    MedFlow EMR

    optometry practice management software

    Next on our list — Medflow EMR software, which was designed to serve as either a standalone EMR (electronic medical record) or as a combination of EMR + practice management (PM) system. Like other optometry practice management software from our list, Medflow EMR was created specifically for eye care, but it can be used by eye care specialists providing both ophthalmology and optometry. 

    Medflow has a bunch of features, but the main one is the software has built-in templates designed for comfortable and time-saving work, including retina scans and surgery, cataracts, glaucoma, digital drawings, eye measurements, LASIK procedures, and more. In addition, it also has a base package, where ASC and optical modules are included.

    Overall, this practice management software will suit a clinic of any size, be it solo practice or a large hospital. The Medflow interface can be easily integrated with other practice management systems or image interpretation applications. Also, the software can be used as a hosted solution or installed on-premise.

    Liquid EHR

    optometry practice management software

    Liquid EHR software will be a perfect solution rather for small and midsize optometry practices than large hospitals. The broad range of its features includes medical records management, medical billing, scheduling and a lot more. The optometry practice management software provides eye care specialists with the ability to generate a mailing list, track systems workflow, manage documents, do compliance checks, integrate e-prescribing, and configurable exam records. 

    What is more, Liquid EHR has a number of specific optometry tools, such as historical IOP charts, drawing tools, built-in eye charts, frames data integration and image management. Optometrists can incorporate lab test results, view clinical summaries and send patient reminders. 

    In addition, the software also allows practitioners to have instant access to electronic insurance filing tools, patient recalls, drug interactions and allergy interaction checks, problem lists, active medication lists, medication recommendations, educational resources, smoking status, vital signs and more.

    EyePegasusEHR

    optometry practice management software

    The EyePegasus optometry practice management software offers a solid number of tools and features for optometry practices. You can schedule appointments online, turn on the automatic appointment reminders, work with a patient portal, scan documents, use an optical calculator and an iOS app with patient check-in features. 

    Using EyePegasus, eye care specialists can customize different tabs by choosing a proper layout, and create templates for treatment documentation. Moreover, optometrists are able to scan medical images and upload them directly into a patient’s chart. The is also a possibility to create referral letters using auto-populated EHR data. Other EyePegasus tools include building and dispensing optical orders and online appointment management. 

    In addition, the optometry practice management software allows managing inventory of different items, such as lenses. EyePegasus also can be integrated with a variety of applications. 

    Eye Cloud Pro

    optometry practice management software

    Another optometry practice management software created for optical professionals is Eye Cloud Pro. The list of its data managing tools is really impressive and includes e-prescribing, inventory management, integrated credit card processing, electronic claims submission, device integrations, two-way texting (SMS), and ECP Billing.

    The system also provides improved patient communication via secure messaging and email services. Clinic managers can configure various appointment types and lets clients request bookings via mobile or desktop devices. The software can be customized mailing lists, referral reports, account information, and sales reports to help with business strategy.

    In addition, one more benefit of Eye Cloud Pro software is that it has an integrated payment processing system with automated invoice and receipt generation. It will make a clinic’s data safe and retained. 

    OD Link

    optometry practice management software

    Taking about comprehensive optometry practice management software, OD Link is one of the most suitable variants for any clinic. It has both PM and EMR/EHR tools, helping to manage patient records, exams, appointments, inventory, billing/insurance information, and much more.

    OD Link software allows optometry practitioners to communicate with patients via SMS or email, work with electronic insurance claim processing centers, and create automated patient entrance forms.

    It also has a mobile app for iOS users, can accept data input from electronic optometry equipment, and can be integrated with different applications.

    ManagementPlus

    optometry practice management software

    Last but not least, ManagementPlus practice management software for optometrists was designed as a fully-fledged and customizable solution with a bunch of functions. With the help of this soft, eye care specialists can work with EHR, PM, ASC forms and inventory. It is also quite helpful in managing revenue cycle services, practice building and reputation management, business analytics and capital funding.

    What is more, ManagementPlus solutions allow optometrists and clinic managers to work in one platform, which makes communication clear and unified. Users can track workflows and handle all billing from eligibility to collections. 

    In addition, ManagementPlus has an in-built reporting tool, which allows specialists to report on most fields in the system, while the practice management system provides a choice of two scheduling modules. Users have the option of choosing either cloud-based or on-premise deployment. 

    Summing up 

    Optometry management software is a perfect choice for any medical practice, including solo practices, midsize clinics, and large hospitals. It is a perfect tool not only for managing patients, optical instruments and aids. The software is also helpful in improving operations, increasing efficiency and revenue and streamlining the working process. Such solutions keep all the data in one place, powering optometrists to document the patient history directly from diagnosis, and managers to avoid unnecessary paperwork.

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    Overall, optometry management software is a need for modern practice, as it improves the diagnosis and treatment, and even can be integrated with image management systems, like Altris AI. This integration assists in managing patient data, helps with controversial OCT scans, differentiate between pathological and non-pathological scans, and, most importantly, gives confidence to eye care specialists.

  • Eye Hospital Management Software: Top 8 Solutions for your Clinic

    Eye Hospital Management Software: Top 8 Solutions for your Clinic

    Maria Znamenska
    04.01.2023
    10 min read

    The term “Eye hospital management software” can have numerous meanings. Some soft can be a part of larger EMR (electronic medical records) systems, some can help with scheduling and billing, and some can help with patients’ information management. There is also an eye clinic management system that can even advise on diagnosis based on the patient’s history and medical images. Because of dozens of different soft on the market, it can be quite complicated to choose a proper set of tools for your practice.

    If you are an ophthalmologist or manage an ophthalmic diagnostic center/hospital, you may have trouble choosing the right software. That is why we’ve decided to prepare a list of solutions for patients’ health recording and diagnosis. We will highlight the benefits of the ophthalmic practice management system and help you choose the right solution.

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    Why eye hospital management software is worth using

    Eye hospital management software has become extremely important for eye clinics or medical centers looking to streamline their workflows, automate processes, and provide higher-quality care with less effort. You can have piles of paper and numerous excels, but when someone is on vacation, it will be impossible to make sense of all data and use it quickly.

    However, many clinics still work according to the old scheme and refuse to introduce new technologies into their clinical practice. There may be several reasons for this: mistrust of modern tools, reluctance to spend the money buying licensed eye clinic management system, or  reluctance to spend staff time learning how to work with the program. But, in fact, today, there are systems designed specifically for ophthalmologists to function flawlessly in eye care settings. Here are some benefits that an eye clinic management system can provide to your medical practice. Let’s take a closer look at some of them:

    Eye hospital management software

    • High level of data protection. Another important benefit of the ophthalmic practice management system is a high level of data protection. High-quality soft gives access to data only to authorized persons. The software also has security systems that guarantee no risk of data loss and full protection of medical history or information about the patient’s condition.
    • Increasing diagnostic accuracy. Using an eye clinic management system, ophthalmologists improve the quality of diagnosis and treatment, as they get access to the whole patient’s history from the past to the present. An ophthalmologist can learn about the previous treatment their patient received and about chronic illnesses. By learning this, doctors can create a better treatment plan.
    • Increased revenue. Depending on the number of employees in your clinic, you may need dozens to hundreds of personnel to smoothly handle manual processes. And more human resources mean more expenses. However, by using best practice management software for ophthalmology, you can significantly reduce spending and let your employees and doctors focus on the more creative tasks that require empathy and communication.   

    These are the most common benefits of an eye clinic management system. However, each system has its unique features, so let’s look at the top 8 eye clinic management systems. 

    Altris AI System

    eye hospital management software

    Altris AI is a unique eye clinic management system that allows eye care specialists to analyze OCT scans with the help of artificial intelligence (AI) tools. 

    How does it work? Putting it simply, retina specialists have colored thousands of OCT scans and named more than 100 retinal pathologies and pathological signs to train an algorithm, so it can assist specialists in detecting the disease. After loading an OCT scan in the eye hospital management software, the AI model evaluates the b-scans (up to 512) and differentiates between normal scans and scans with moderate and severe pathology. It gives eye care professionals the ability to focus only on serious (red) scans, saving patients valuable time.

    In addition, Altris AI allows its users to see a broader perspective of a patient’s eye health. All the reports are dynamically editable: the ophthalmologist can add/revise/delete items in the OCT report. Eye care specialists also can add segmentation/classification results to the OCT report in 1 click. And what’s even more important, Altris AI OCT report is understandable for both ophthalmologists and patients. 

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    Eye clinic management system features of Altris AI

    • The system allows working with all popular OCT equipment and all data storage formats, including DICOM of various lengths, png, and jpg.
    • Altris AI ophthalmic practice management system can be easily integrated with EHR systems or run standalone as a web application.
    • The system also takes care of user security, as all important patient data is tokenized and protected from disclosure at all stages.
    • The artificial intelligence program can independently identify more than 100 retinal pathologies and pathological signs.
    • The Smart Reports feature allows ophthalmologists to select the elements (single scan, layers, both eyes, etc.) that they want to see in their OCT report.
    • This All Scans feature allows the user to view all scans of a single OCT examination, sort them by severity level, and zoom.

    Watch a short overview of how Altris AI assists eye care specialists with OCT diagnosis and decision-making.

     

    DrChrono Software

    eye hospital management software

    DrChrono EHR is an iPad and iPhone-compatible platform that offers fully customizable form templates or ready-made forms to help users track patient information. 

    DrChrono EHR is an iPad and iPhone-compatible platform that offers fully customizable form templates or ready-made forms to help users track patient information. 

    Eye clinic management system features of DrChrono Software

    • The system allows medical practices to manage patient admissions, patient care, clinical charts, and billing.
    • Healthcare professionals can add patient notes to the medical record. The Vital Flowsheets module provides the ability to create basic health data and monitor the health indicators of each patient.
    • The DrChrono eye hospital management software also offers a variety of application integrations. 
    • Doctors can use the Free Draw module to annotate charts, OCT scans, or other files.

    RXNT Software

    eye hospital management software

    RXNT is a comprehensive billing, practice management, and EHR solution. This system improves patient care and simplifies clinical management. Access patient health history and prescriptions at the point of care, schedule patients and providers, and request and review lab or imaging orders with multi-site single sign-on (SSO).

    Eye clinic management system features of RXNT Software

    • Any RXNT ophthalmic practice management system products (EHR, ERX, PM, Billing, Scheduling) can be combined into a fully integrated “Full Suite” system.
    • Ophthalmologists, managers, or staff can add and organize documents in patient charts for clinical care plans and follow-up.
    • The system has developed customizable “smart” forms and short keys that improve work processes.
    • RXNT can share real-time data with other doctors to better coordinate care and support.

    In addition, an ophthalmic clinic can integrate RXNT eye hospital management software with the Altris AI system to create and dynamically edit OCT reports.

    Medfiles Software

    eye hospital management software

    Medfiles Software is a multi-task cloud-based solution that ensures compliance for ophthalmology clinic employees. The key features of this eye hospital management software are drug screening management, medical record tracking, case management, training tools, reporting, and safety documentation.

    Eye clinic management system features of Medfiles Software

    • Medfiles tracks patient treatment plans, open cases, treatment plans, medical expenses, and cash reserves and creates conclusions based on all the information.
    • The system can be easily integrated with different software so a doctor or staff can see scans of specific OCT examinations.

    Medfiles eye clinic management system allows to compare annual summary reports with benchmarks.

    IntelleChartPRO Software

    eye hospital management software

    Another cloud-based ophthalmic electronic medical record (EMR) solution is IntelleChartPRO. This system is very popular among ophthalmology clinics and centers. IntelleChartPRO helps professionals record and manage a patient’s treatment and medical history more effectively.

    Eye clinic management system features of IntelleChartPRO Software

    • Physicians or ophthalmology clinic management can customize the EHR themselves to fit their unique workflows.
    • IntelleChartPRO eye hospital management software developed adaptive template technology that allows offices to generate templates for each patient.
    • In combination with other eye clinic management system tools, the software becomes more relevant and allows more accurate diagnoses of patients and the creation of detailed reports.

    MaximEyes Software

    eye hospital management software

    MaximEyes is a comprehensive, unified electronic health record (EHR) and practices management solution designed exclusively for ophthalmology practices. It has a modern and intuitive user interface. The system will work on any computer OS. If users do not want to use cloud technologies or the clinic has a weak Internet connection, MaximEyes can be deployed through a local server

    Eye clinic management system features of MaximEyes Software

    • For each patient, the system allows ophthalmologists to set up an individual template according to different types of visits.
    • The eye hospital management software EHR includes a flexible rules engine that will suggest or automatically generate post-diagnosis codes, procedure codes, and output documents.
    • The First Insight module also offers an ophthalmic imaging management solution that works with any EHR.

    75health Software

    eye hospital management software

    One more fully-fledged eye clinic management system is 75health, which is also a cloud-based solution that provides its users with electronic health record tools. 75health system will be most suitable for managing health records and patient information for ophthalmologists working in small and mid-sized clinics.

    Eye clinic management system features of 75health Software

    • 75health eye hospital management software allows ophthalmic clinic staff to download and save patients’ medical images, such as consent forms, handouts, or scans.
    • Doctors can also create a treatment plan for their patients right in the system and scan records for allergies, medications, lab results, and symptom lists.
    • 75health solution provides smooth integration of ophthalmic management systems, which helps ophthalmologists in decision-making.

    myCare Integrity Software

    eye hospital management software

    Another cloud-based eye hospital management software that is worth your attention is myCare Integrity. It was created specifically for eye care specialists and contains a strong set of tools and modules that can cover the needs of any member of the ophthalmic clinic staff: from doctors to managers.

    Eye clinic management system features of myCare Integrity Software

    • The myCare Integrity system has an IntegriVIEW functionality that allows practitioners to link medical images directly to every screen of EMR.
    • There is also an IntegriDRAW module inside the eye clinic software, where templates are included in the application. It allows users to rely on the previously created stamps.
    • The IntegriLINK module allows ophthalmologists to link the diagnostic equipment to the system.
    • What is more, myCare Integrity eye hospital management software allows you to customize and personalize the dashboard.

    Summing up

    Eye hospital management software is extremely important for any clinic, whether there are 10 or 500 employees. It can help you improve your workflow by keeping a lot of data in one place. Imagine how easily you can get rid of unnecessary paperwork, forget about administrative costs, and speed up processing. In addition, with an ophthalmic practice management system, you can get 24/7 access to patients’ data.

    AI for OCT Analysis

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    However, the key benefit of practice management software for opticians is the improvement of diagnosis and treatment. There are already ophthalmic image management systems, like Altris AI, that can not only help to manage patients’ data but also provide a second opinion regarding medical image analysis. Using this knowledge, doctors can have better access to patients’ health problems and reports, ultimately enabling them to provide the best care to their clients.

  • Application of ML in ophthalmology

    The Application of Machine Learning in Ophthalmology: The View from the Tech Side

    Philip Marchenko
    30.11.2022
    15 min read

    According to the World Health Organization (WHO), artificial intelligence (AI) and machine learning (ML) will improve health outcomes by 2025. There are numerous digital technologies that shape the health of the future, yet AI and machine learning in ophthalmology and medical image analysis look like one of the most promising innovations.

    The healthcare industry produces millions of medical images: MRI, CT, OCT, images from the lab, etc. The right diagnosis depends on the accuracy of the analysis by the specialists. Today AI can back up any medical specialist in medical image analysis: providing confidence and much-needed second opinion.

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    Check how artificial intelligence assists in OCT interpretation

    Altris AI team decided to improve medical image analysis for just one type of medical image: Optical Coherence Tomography scans of the retina. To do it, the Altris AI team collected thousands of OCT scans and graphically labeled them, defining more than 100 pathologies and pathological signs. Watch the video to discover more features of Altris AI platform. 

    Then all this data was fed into the AI model. Further, I will tell how exactly we train the AI model of Altris AI so that it can detect more than 100 pathologies with 91% accuracy, but first, let’s discuss why it is important for the healthcare industry.

    Why are automation and machine learning in ophthalmology important?

    Due to the delicate anatomy of the eye, its treatment carries a high risk of complications. Sometimes these complications can be the result of a medical error by an eye care specialist. But how often?

    According to the Altris team research, 20.2% of eye care practitioners miss minor, early, and rare pathologies on OCT scans 1- 3 times a week, and 4.4% miss them 3-5 times a week. But the worst thing is that 30.5% of ophthalmologists and optometrists are not even sure if they are missing any pathology at all.

    Some medical errors may be minor, but some may cause significant harm to patients. Such medical errors can lead to medical malpractice lawsuits. That is why most ophthalmic clinics consider implementing AI to double-check the diagnosis of the ophthalmologist. 

    Besides, different tools of machine learning in ophthalmology have a high level of accuracy and can provide eye care specialists with a second opinion. 

    How to reach a high level of accuracy?

    It is almost always necessary to conduct many experiments to achieve a high level of model accuracy (in the case of Altris AI, it is 91%).  It is often done with the help of a machine learning pipeline.

    machine learning in ophthalmology

    High level of ML pipeline accuracy

    The machine learning pipeline is programmed by a team of engineers to perform certain steps automatically. It systematically trains and evaluates models, monitors experiments, and works with datasets.

      1. ML and Medical teams collect, annotate and preprocess data. It’s crucial to ensure the data quality is at its highest level because the model’s quality heavily depends on it. To do this, the teams developed a process and annotation guideline, which ensures that the number of errors in the annotation is minimized.
      2. ML team chooses the appropriate approach (model) depending on the collected data and the tasks. Each team member is well-versed in the most modern and high-quality approaches that solve emerging tasks.
      3. The selected model is trained on the annotated data.
      4. In the model evaluating and testing stage, we develop tests aimed at helping us understand whether the model is trained properly to perform the needed tasks.
      5. After the ML team is satisfied with the result, we deploy the model, which means the model is ready for production.
      6. While the model is running in production, we monitor its performance to ensure everything goes well.

    This workflow allows engineers to continuously fine-tune existing models alongside constant performance evaluations. The most significant advantage of this process is that it can be automated with the help of available tools. 

    Try Altris AI for free

    Check how artificial intelligence assists in OCT interpretation

     

    What tasks does machine learning in ophthalmology have?

    Within the Altris AI platform, we solve 2 main tasks: segmentation and classification of OCT scans. 

    Classification task

    Classification is the task of determining which category a particular object belongs to. We assign each pathology to a certain class of pathologies (for example, glaucoma class).

    Segmentation task

    The image segmentation problem can be stated as the division of an image into regions that separate different objects from each other, and from the background.

    Key metrics of Altris ML pipeline

    When discussing classification and segmentation metrics in medical imaging machine learning, it is essential to mention the Confusion matrix (CM). CM is a visualization of our performance, which helps us understand whether the model is performing well in terms of predicted and real data. For a better explanation, let’s take a look at the picture. 

    machine learning in ophthalmology

    Let’s consider 4 possible outcomes from model predictions. Say we need to create a classifier to diagnose or predict if a patient has a disease (positive / 1 or TRUE) or not (negative/ 0 or FALSE). In such a case, the model can predict “yes” or “no”, and we can have an actual “yes” or “no”. Based on this, we can get 4 categories of results:

    • TP — true positive. The patient that actually has a disease has been diagnosed with this disease. A class was predicted to be true, and it is actually true.
    • TN — true negative. The patient is actually healthy and has been diagnosed as healthy. A class was predicted to be false, and it is actually false.
    • FP — false positive (type 1 error). The patient that is actually healthy has been diagnosed as having a disease. A class was predicted to be true, but it is actually false.
    • FN — false negative (type 2 error). The patient that actually has a disease has been diagnosed as healthy. A class was predicted to be false, but it is actually true.

    With the help of the confusion matrix, our ML engineers get specific metrics needed to train our medical imaging machine learning model properly. We discuss each metric in more detail in the following paragraphs.

    Classification metrics

    • Accuracy

    To find out how many of our predictions were correct, we divide the number of correct predictions by the total.

    machine learning in ophthalmology

    While being intuitive, the accuracy metric heavily relies on data specifics. If the dataset is imbalanced (the classes in a dataset are presented unevenly), we won’t get trustful results.

    For example, if we have a training dataset with 98% samples of class A (healthy patients) and only 2% samples of class B (sick patients). The medical imaging machine learning model can easily give you 98% training accuracy by predicting that every patient is healthy, even if they have a disease. Such results may have destructive consequences as people won’t get needed medical treatment.  

    • Precision

    Precision shows what proportion out of all positive predictions was correct.

    machine learning in ophthalmology

    Precision metric helps us in cases when we need to avoid False Negatives but can’t ignore False Positives. A typical example of this is a spam detector model. As engineers, we would be satisfied if the model sent a couple of spam letters to the inbox. However, sending an important non-spam letter to the spam folder (False Positive) is much worse.

    • Sensitivity/Recall

    Recall shows how many of all really sick patients we predicted and diagnosed correctly. It is a proportion of correctly positive predictions out of all positives.

    machine learning in ophthalmology

    In our case, you want to find all sick people, so it would not be so critical if the model diagnoses some healthy people as unhealthy. They would probably be sent to take some extra tests, which is annoying but not critical. But it’s much worse if the model diagnoses sick people as healthy and leaves them without treatment. 

      The sensitivity of Altris AI is 92,51%

    • Specificity

    The specificity shows how many of all healthy patients we predicted correctly. It is the proportion of actual negatives that the medical imaging machine learning model has correctly identified as such out of all negatives.

    machine learning in ophthalmology

    Specificity should be the metric of choice if you must cover all true negatives and you can’t tolerate any false positives as a result. For example, we’re making a fraud detector model in which all people whose credit card has been flagged as fraudulent (positive) will immediately go to jail. You don’t want to put innocent people behind bars, meaning false positives here are unacceptable. 

    The specificity of Altris AI is 99,80%

    Segmentation metrics

    Segmentation also can be thought of as a classification task. For each pixel, we make predictions about whether it is a certain object or not. Therefore, we can talk about Accuracy, Precision, Recall, and Specificity in terms of segmentation. 

    Let’s say we have a Ground Truth (what is really an object) and a Segmented (what the model predicted). The intersection in the picture below is the correct operation of the medical imaging machine learning model. All that is the difference (FN and FP) is the incorrect operation of the model. True negative (TN) is everything the model did not mark in this case.

    machine learning in ophthalmology

    Quite often, even after looking at such metrics, the problem of non-symmetricity remains in the segmentation tasks. For example, if we consider a tiny object, the Accuracy metric doesn’t work. Therefore, segmentation tasks also refer to additional metrics that allow taking into account the size of the object of the overall quality assessment. Let’s look at additional metrics in more detail.

    • Intersection over Union (IoU)/Jaccard

    Intersection over Union is an evaluation metric used to measure segmentation accuracy on a particular image. This metric is considered quite simple — the intersection zone is divided by the union of Ground Truth and Segmented.

    machine learning in ophthalmology

    Sometimes we get such results, like if the object was determined to be very large, but in fact, we see that it is small. Then the metric will be low, and vice versa. If the masks are approximately equal to each other, everything works correctly, and the metric will be high.

    • Dice score/F1

    The dice coefficient is 2 times the area of overlap divided by the total number of pixels in both images.

    machine learning in ophthalmology

    This metric is a slight modification of the previous one. The difference is that, in this case, we take the intersection area twice.

    Calculating scores over dataset

    We calculate the metrics described above for each scan. In order to count them over the entire dataset, we take each picture in this dataset, segment it, calculate the metric, and then take the average value of the metrics on each image.

    What is model validation in ML?

    In addition to evaluating the metrics, we also need to design the model validation procedure suitable for a specific task.

    When we have determined the metric that suits the task of machine learning for medical image analysis, we also need to understand what data to use for calculation. It will be wrong to calculate the metric on the training data because the model has already seen it. This means that we will not check the ability of the model to generalize in any way. Thus, we need a specific test dataset so that we can carry out quality control according to the selected metrics.

    The main tasks of the model validation are:

    • To provide an unbiased estimation of the accuracy that the model can achieve
    • To check whether the model is not overfitted

    Picking the correct model validation process is critical to guarantee the exactness of the validation method. In addition, there is no single suitable validation method for machine learning in ophthalmology — each task requires different validation. Engineers separately examine each task to see if data has leaked from the train dataset to the test dataset because this may lead to an overly optimistic estimate of the metrics.

    For example, we can take OCT images in different resolutions. We may need a higher image resolution for some diseases. If the medical imaging machine learning model overfits at the resolution of this OCT, it will be called a leak because the model should behave the same at any resolution.

    Overfitting and underfitting

    The model also has such an important property as a generalization. If the model did not see some data during training, it should not be difficult for the model to determine which class a certain image belongs to.

    At this stage, engineers may have two problems that they need to solve. The first problem is overfitting. When the model remembers the training data too well, we lose the ability to make correct predictions. The picture below illustrates this problem. The chart in the middle is a good fit when the model is general enough and has a positive trend, and the trend is well-learned. But the chart on the right shows a too-specific model that will not be able to guess the trend.

    machine learning in ophthalmology

    Another problem to solve is underfitting. This problem arises when we have chosen a model that is not complex enough to describe the trend in the data (left chart).

    Bias variance trade-off

    Another important concept we use in machine learning model validation is the bias variance trade-off. We want our models to always make accurate predictions and have no ground truth scattered. As shown at first/second circle.

    However, there are situations when we have a model that predicts something close to the target, but from dataset to dataset, it has a strong scatter. This is showcased in the second circle. 

    In circle three, you can observe a situation where the model has heap predictions on different datasets, but they are inaccurate. This situation usually indicates that we need to almost entirely rebuild the model.

    machine learning in ophthalmology

    Overfitting and bias variance trade-off are very important in working with the model, as they allow us to track errors and select a model that will balance between spread and hitting the target.

    Unbiased estimation

    In addition, within each model, we evaluate a set of parameters. We made a certain estimate (graph on the left), but in real life, the distribution of parameters differs (graph on the right). Thus, seeing that our estimate turned out to be shifted, we find another problem that needs to be solved. Machine learning in ophthalmology needs to make the estimate as unbiased as possible.

    machine learning in ophthalmology

    How do we validate the Altris AI model?

    There are three main steps in choosing a validation strategy for machine learning in ophthalmology:

    • we got familiar with ophthalmology, understood the nature of data, and where the leakages are possible;
    • We estimated the dataset size and target distribution;
    • understood the model’s training complexity (amount of operations/ number of parameters/ time) to pick the validation algorithms.

    After that, we have a reliable strategy for the machine learning model validation. Here are some fundamental concepts we use in the validation of models’ performance.

    Train/test split

    Train/test split is the most simple and basic strategy that we use to evaluate the model quality. This strategy splits the data into train and test and is used on small datasets. For example, we have a dataset of 1000 pictures, 700 of which we leave for training and 300 we take for the test.

    This method is good enough for prototyping. However, we don’t have enough datasets with it to do a simple double-check. This phenomenon is called high sampling bias: this happens when we encounter some kind of systematic error that did not fit into the distribution in the train or test.

    By dividing data into train and test, we are trying to simulate how the model works in the real world. But if we randomly split the data into train and test, our test sample will be far from the real one. This can be corrected by constructing several test samples from the number of data we have and examining the model performance. 

    Train/test/holdout set

    We leave the holdout as the final validation and use the train and test to work with the medical imaging machine learning model. After optimizing our model on the train/test split, we can check if we didn’t overfit it by validating our holdout set.

    machine learning in ophthalmology

    Using a holdout as a final sample helps us look at multiple test data distributions and see how much the models will differ.

    K-fold cross validation

    There is also a more general approach that Altris AI team use for validation — k-fold cross validation. This method divides all of our data equally into train and test.

    machine learning in ophthalmology

    We take the first part of the data and declare it as a test, then the second, and so on. Thus, we can train the model on each such division and see how it performs. We look at the variance and standard deviation of the resulting folds as it will give information about the stability of the model across different data inputs.

    Do we need ML models to perform on par with doctors?

    Here I will try to answer a question that worries many ophthalmologists and optometrists: can machine learning for medical image analysis surpasses an eye care specialist in assessing quality?

    In the diagram below, I have drawn an asymptote called the Best possible accuracy that can be achieved in solving a particular problem. We also have a Human level performance (HLP), which represents how a person can solve this problem. 

    HLP is the benchmark that the ML model strives for. Unlike the Best possible accuracy, for which there is no formula, HLP can be easily calculated. Therefore, we assume that if a model crosses the human quality level, we have already achieved the best possible quality for that model. Accordingly, we can try to approximate the Best possible accuracy with the HLP metric. And depending on this, we understand whether our model performs better or worse.

    machine learning in ophthalmology

    For those tasks that people do better and the ML model does worse, we do the following:

    • collect more data
    • run manual error analysis
    • do better bias/variance analysis

    But when the model crosses the HLP quality level, it is not entirely clear what to do next with the model and how to evaluate it further. So, in reality, we don’t need the model to outperform a human in interpreting images. We simply won’t know how to judge the quality of this model and whether it can be 100% objective and unbiased.

    Avoidable bias

    Let’s say we need to build a classifier for diabetic retinopathy based on OCT scans, and we have a control dataset prepared by people. In the first case, doctors are wrong 5% of the time. At the same time, the model on the train set is wrong in 10% of cases and on the test set — in 13%.

    machine learning in ophthalmology

    The difference between the model’s and the human’s performance is usually taken as the minimum difference between the train/test set and the human. In our case, it is 5% gap (10% – 5%) of avoidable bias. It is called avoidable bias because it can be fixed theoretically. In such a case, we need to take a more complex model and more data to better train the model.

    In the second case, doctors determine the disease with a 9% error. If the model defines a disease with the same rates as the previous example, then the difference between the train/test set and the human will be 1% (10% – 9%), which is much better than avoidable bias

    Looking at these two cases, we must choose a strategy that will lower the variance for the machine learning model so that it works stably on different test sets. Thus, taking into account the avoidable bias and the variance between the samples, we can build a strategy for training the model so that it could potentially outperform the HLP someday. However, do we need it now?

    Understanding HLP

    To better understand the HLP metric, let’s consider the task of determining dry AMD on OCT scans. We have a fixed dataset and 4 train sets, each one determining dry AMD with a specific accuracy:

    • ML engineers – 20%
    • ophthalmologists – 5%
    • 2 ophthalmologists – 3%
    • 2 ophthalmologists and 1 professor of ophthalmology – 2%

    machine learning in ophthalmology

    We take a result of 2% as the best HLP possible. To develop our model, we can choose the performances we strive to get. The 20% error result is irrelevant, so we discard this option. But the level of 1 doctor is enough for model version number 1 model. Thus, we are building a development strategy for model 1.

    Summing up

    Machine learning will revolutionize the eye care industry. It provides confidence and second opinion to eye care specialists in medical image analysis. 

    If you are looking for ways to use machine learning in your eye care practice, feel free to contact us. At Altris AI, we improve the diagnostic process for eye care practitioners by automating the detection of 54 pathological signs and 49 pathologies on OCT images.

  • AI in ophthalmology for academic purposes, announcement of strategic partnership, cover

    Altris AI Builds Partnership with Academic Institutions

    Maria Znamenska
    21.11.2022
    2 min read

    AI in ophthalmology for academic purposes

    Aston University and Altris AI join forces to Revolutionise Optometry Education

    We are proud to announce our new strategic partnership with Aston University, a renowned healthcare education and research institution. This partnership marks a significant step forward in enhancing the preparation and training of optometry students with the help of AI technology.

    As OCT examination proves to be one of the most accurate and yet the most complex diagnostic devices in the eye care industry, it is crucial for educational institutions to stay ahead and equip their students with the most innovative tools, such as artificial intelligence. The collaboration between ourselves and Aston University will enhance how optometry students learn and improve OCT interpretation skills.

    Aston University, known for its commitment to excellence in healthcare education, has chosen to partner with Altris AI to integrate AI-driven solutions into future optometrists training in the lecture theatre, university clinics, and research departments. The Altris AI platform will also be used in the study of Ph.D.-level research projects.

    Commenting on this exciting partnership, James Wolffsohn, Head School of Optometry said “We strive to equip our students with cutting edge knowledge and tools to deliver world class eyecare to their patients. This partnership with Altris AI will help strengthen our students diagnostic ability and keep on the crest of the innovation wave offered by AI”

    Maria Znamenska, MD, Ph.D., Associate Professor of Ophthalmology and a Chief Medical Officer at Altris AI, expressed her enthusiasm, stating, “The new generation of optometry students ask for modern ways of learning. Today they want more than books and atlases, they want to learn interactively and utilise the power of technology in clinical practice. And we are happy that AI has become a true copilot for the young generation of optometry students at Aston University.”

    This partnership is a testament to the commitment of both organisations to innovation in healthcare education. Together, Aston University and Altris AI aim to shape the future of optometry education and empower students to provide excellent level eye care services to patients. After all, the ultimate goal of digitalisation in healthcare is always healthier patients.

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  • will optometrists be replaced by ai

    Will Artificial Intelligence Replace Ophthalmologists & Optometrists?

    Maria Znamenska
    17.11.2022
    8 min read

    Will optometrists be replaced by AI?

    Back in 2019, at the World Science Congress, Peter van Wijngaarden, Deputy Director of the Center for Eye Research, claimed that the eye sector is one of the leading areas of medicine in terms of artificial intelligence (AI) implementation. According to RANZCO, AI systems are already achieving incredible results and, in some cases, can even rival eye care specialists.

    Since AI has become a buzzword, there are hundreds of articles, speeches, and videos on this topic. We are the company that created AI for the eye care and we know the answer to this question: ” Will AI take over optometry and ophthalmology?” ( Spoiler: NO).

    Try a co-pilot AI for OCT analysis ( but it won't replace you)

    It is simple a misconception. There are a lot of similar examples of AI misconceptions when famous professors and specialists in the field of ophthalmology made predictions that artificial intelligence is rapidly gaining strength in the eyecare industry. This gives rise to many myths and fears around the introduction of AI in clinical practice. Will optometrists be replaced by AI? What about ophthalmologists? What is going on?

    The increased attention to the issue of optometrists and ophthalmologists replaced by AI was also provoked by a World Economic Forum (WEF) report. According to this report, people can lose 85 million jobs by 2025 due to the shifting division of labor between people and machines.

    In this post, we will discuss the top 5 AI misconceptions that are most often faced by the owners of ophthalmological clinics and optometry centers in order to dispel them once and for all.

    What exactly is AI? Do AI algorithms work exactly like a human brain?

    artificial intelligence replace ophthalmologists

    The concept of optometrists and ophthalmologists replaced by robots is gaining popularity. Nowadays, eye care specialists often discuss the potential of AI training in human cognitive skills. It is no longer just about the ability of AI to detect Diabetic retinopathy or interpret OCT scans with greater accuracy. The question is, will AI ever be able to replicate human consciousness? And can AI replicate how the human brain works?

    What do we know about such models in different areas? AI systems are already demonstrating the work of some human cognitive functions. For example, AI models successfully compete with humans in computer games by gradually learning successful strategies. There is also an AI ​​model which creates enjoyable melodic music.

    However, replacing optometrists and ophthalmologists with AI still seems  VERY unrealistic. Even with the above examples mimicking some aspects of human behavior, an AI algorithm still needs to learn what empathy is. Artificial intelligence does not understand and cannot make sense of its surroundings, nor can it learn from its surroundings as humans do. The most famous example that confirms this inability of AI is Siri or Alexa. Voice assistants can set up appointments but give strange answers when the conversation goes differently than their scenario.

    While the human brain inspires modern AI techniques such as neural networks (NNs), the structure of NNs architectures is not biologically realistic. 

    First of all, there is a set of qualities that ophthalmologists and optometrists use every day. It is empathy for the patient, as well as creativity, teamwork, and adaptability. These qualities help doctors provide effective care to their patients. It is unlikely that the machines will ever be able to work with children, older adults, or patients with specific disabilities on par with humans. In addition, any patient would like to hear the diagnosis or discuss a treatment plan with a doctor, not a machine. 

    Therefore AI algorithm can’t work like a human brain, and the scenario where artificial intelligence replace ophthalmologists and optometrists will never happen. Nowadays, there are no developments that would make us think that AI image interpretation will ever be able at least to repeat important qualities of eye care specialists.

    Is today’s state of AI dangerous for humans?

    artificial intelligence replace ophthalmologists

    Today, AI algorithms can interpret retinal images and distinguish pathological from non-pathological scans. However, not all attempts at AI implementation have succeeded as well. One of the most popular non-medical examples is Facebook. Some time ago, Facebook tried to identify relevant news for certain groups of users. But the automated process could not detect the difference between real and fake news. Russian hackers managed to trick the system and bypass automatic filters. They posted fake news, forcing the Facebook team to come back to human editors.

    This is just one example of how security lags behind performance when humans rely on AI too much. Artificial intelligence is a great tool, but in most cases, its abilities only give reliable and the most accurate results in collaboration with eye care professionals. Although machines are designed by humans, they often can’t predict human behavior and don’t know how to cope with situations or clinical cases that go beyond the scope of the algorithm.

    Therefore AI is not dangerous for humans when ophthalmologists and optometrists periodically control the work of algorithms and review how the machine works. This is the number-two reason why artificial intelligence replace ophthalmologists and optometrists is unrealistic.

    Will AI ever be 100% objective?

    artificial intelligence replace ophthalmologists

    To honestly answer the questions of will artificial intelligence replace ophthalmologists and optometrists and whether it is 100% objective, you need to understand that an AI system will only be as good as its inputs. By loading unbiased training datasets, engineers can create an AI system that makes unbiased decisions. However, in the real world, AI is unlikely ever to be 100% objective. 

    For example, many well-known companies, such as Amazon or Facebook, still struggle with the gender gap in hiring. Some time ago, Amazon used historical data from the past ten years to train its AI recruiting model. The algorithm was supposed to process data and candidates and free recruiters from the routine viewing of hundreds of CVs. However, soon Amazon team discovered that the data was biased against women. AI algorithm was trained by outdated information when the technology industry used to be dominated by men. Thus, the new recruitment system selected only male candidates. This forced Amazon to abandon the algorithm and re-open many recruiter positions.

    In the field of ophthalmology, AI models can already accurately predict diabetes risk factors or potential vision loss from OCT images. So when will artificial intelligence replace ophthalmologists? In Altris, we are sure the algorithm will never achieve adequate objectivity, as it will always be limited by input data, whether demographics, gender, or age. 

    Now we know that AI can’t be 100% objective. Indeed, ophthalmologists and optometrists can’t match the ability of algorithms to detect pixel-level patterns among the millions of pixels in the OCT scan. However, only the cooperation of eye care specialists and a quality AI model working together will allow for more accurate detection of diseases. The combined efforts of AI management systems and eye care specialists can help achieve the desired 100%.

    Will optometrists be replaced by AI?What about ophthalmologists?

    artificial intelligence replace ophthalmologists

    Various articles have speculated on whether artificial intelligence replace ophthalmologists and optometrists, raising concerns about unemployment. However, this never corresponded to the actual state of affairs. Carl Benedikt Frey, an Oxford Martin Citi Fellow at Oxford University, reported that while 47% of jobs are at risk of automation, the risk for doctors is estimated at only 0.4%.

    In addition, in his book “Humans Are Underrated”, Geoff Colvin states that the most valuable skill for ophthalmologists is the ability to sense the thoughts and feelings of patients who are losing sight.

    Many patients complain about the lack of contact with the doctor. They admit that the treatment would be more comfortable if doctors devoted more time to live communication. This mainly applies to children and the elderly, who need a lot of attention from eye care specialists. Empathy and similar human qualities are not only an understanding of the patient’s feelings but also an adequate response to them. Thus, a future in which optometrists and ophthalmologists are replaced by AI seems senseless.

    Professor Tien Yin Wong, medical director of the Singapore National Eye Centre, claimed that AI holds great promise for retinal screening. And while AI for OCT interpretation will radically change clinical practice, the technology’s more significant impact will be to complement and enhance human capabilities rather than replace them. The field of ophthalmology demonstrates that the combined efforts of scientists and machines are more effective than either could achieve individually. 

    Artificial intelligence for OCT interpretation is just a recommendation system for an eye care specialist. Often one pathological sign, for example, Cystoid macular edema (CME), or Intraretinal fluid, can indicate many diseases, like Wet AMD, DR, DME, CRVO, and others. That is why AI is only an assistant to a doctor, especially when it comes to rare pathologies.

    All in all, AI for OCT interpretation is just a tiny part of clinical practice and can never work without humans. In order to detect the pathological signs and diagnose a disease correctly, an eye care specialist must perform different examination methods. Among these exams are visual acuity, intraocular pressure, ophthalmoscopy, and a basic patient examination, which includes anamnesis. Moreover, ophthalmologists and optometrists may also need to perform other visualization methods, like Fundus photography, FFA, or OCTA.

    Will AI replace optometrist?

    This is probably one of the key AI misconceptions. Automation has led to a significant change in many industries, and ophthalmology is no exception. So when will AI take over optometry and ophthalmology?  The answer is quite simple — AI will never replace eye care specialists. It will eventually take over routine tasks, allowing the careers of ophthalmologists and optometrists to advance in new and exciting directions.

    Try a co-pilot AI for OCT analysis ( but it won't replace you)

    Automated interpretation of OCT scans will significantly increase the circulation of patients in ophthalmic clinics or optometry centers, which is commercially attractive. Moreover, with increasing life expectancy, and expanding the range and effectiveness of treatment options offered, a collaborative effort between ophthalmologists and AI will improve patient outcomes. This will make ECPs more efficient, freeing up time for human interaction between doctor and patient, which has been a cornerstone of medicine for decades.

    There are hundreds of eye care specialists who are already using AIf for OCT scan analysis, for example, to improve! the results. So will AI take over optometry or ophthalmology? The answer is rather simple: No

    will ai take over optometry