The AI Advantage: Earlier, More Accurate OCT Insights

Have you ever finished writing a document and run a spelling and grammar check just to make sure you didn’t miss anything? Almost 100% of the time, the spelling and grammar check will find something, even for those that are exceptionally thorough. It’s a rare occasion (or a really short document) if there are no corrections.

Now imagine having that same kind of intelligent “double-check” when interpreting an optical coherence tomography (OCT) scan. Wouldn’t it be just as useful, if not more, to have a similar tool when interpreting an OCT image?

The Transformative Power of OCT—and Its Growing Complexity

OCT has transformed how we care for patients and emerged to become an indispensable tool with approximately 75% of practices reporting they own one (and the rest reporting planning to get an OCT as the item on top of their shopping list).¹  OCT imaging allows us to better diagnose retinal and optic nerve conditions and monitor treatment response over time. Without OCT, some conditions, such as vitreomacular disorders, subtle retinal layer changes, or early macular changes, could easily go unnoticed. Yet, despite all our experience and training, we remain human: even the most seasoned clinicians can miss minute features, misinterpret scans, or struggle when multiple conditions coexist.

Add to that the fact that we have accumulated an extensive body of knowledge about OCT imaging interpretation and biomarkers. However, learning all these nuances has also added to the burden on clinicians interpreting OCT scans. Even for experienced retina specialists, subtle findings can be overlooked. Just recently, while writing a lecture on OCT biomarkers, I came across several that I did not know about. The truth is, even the best of us can miss things when interpreting an OCT. Wouldn’t it be reassuring to have a backup system to double-check or confirm your interpretation? A tireless assistant that doesn’t suffer from fatigue, bias, or preferential attention. That’s exactly where artificial intelligence comes in. Unlike humans, AI does not struggle with preferential looking or cognitive biases. AI can consistently examine every pixel of a scan, detect multiple coexisting pathologies and quantify changes objectively over time.

The Limitations of Human Interpretation—and How AI Fills the Gap

Even with years of expertise, clinicians face several real-world challenges when interpreting OCT images:

1. Cognitive Load and Oversight. The number of OCT biomarkers and disease patterns continues to expand. No single clinician can track all of them perfectly.

2. Preferential Attention. When a patient has multiple comorbid retinal diseases, one pathology can distract from another. AI doesn’t get tunnel vision.

3. Variability in Experience. A provider who sees mostly primary care may interpret OCT differently than a high-volume retina specialist. AI helps level the playing field.

4. Limitations of Normative Databases. Current OCT devices rely heavily on built-in normative databases. But those datasets may not represent the demographics of the region or population you treat. Misalignment can reduce diagnostic accuracy or create false-positives and false-negatives. AI assisted image interpretation offers a new kind of support that adapts, learns and analyzes images with precision, regardless of demographics, fatigue, or experience level.

How AI Is Enhancing OCT Interpretation Today

AI solutions for retinal imaging now provide a suite of capabilities that go far beyond simple automated thickness maps. Modern AI platforms can:

• Identify disease-specific structural biomarkers
• Track progression or treatment response with objective, longitudinal analysis
• Flag subtle or rare abnormalities that a clinician may overlook
• Segment multiple retinal layers far more precisely than standard device algorithms
• Offer differential-diagnosis suggestions based on image patterns
• Reduce interpretation variability between clinicians and across visits

This evolution parallels how grammar checkers evolved from basic spell-check to advanced language modeling. For example, platforms like Grammarly do not just correct your grammar. They help improve the structure and meaning of your writing. Likewise, AI-assisted OCT image interpretation doesn’t just measure thickness, it helps clinicians better understand the meaning of what they see.

The Role of AI in OCT Imaging

AI deep learning  algorithms can analyze complex OCT volumes in seconds, highlight relevant biomarkers and flag subtle changes that might otherwise be missed by the human eye. The applications are numerous:

• Retinal disease detection and quantification: Deep learning models can identify macular edema, drusen, geographic atrophy, epiretinal membranes and choroidal neovascularization with sensitivity and specificity comparable to expert retina specialists. They can even segment retinal layers and quantify volumes, giving clinicians reproducible metrics to track disease over time.
• Glaucoma diagnosis and monitoring: AI algorithms can analyze retinal nerve fiber layer (RNFL) and ganglion cell complex asymmetry, detect early structural changes in the optic nerve head and predict disease progression with high accuracy. For patients at risk of glaucoma, AI provides an objective lens through which subtle damage can be tracked over time.
• Longitudinal analysis: AI can compare a patient’s OCT scans over months or years, providing trend data on layer thickness, fluid accumulation, or lesion growth. This can guide treatment decisions, such as determining when to initiate or escalate therapy in neovascular age-related macular degeneration (AMD) or diabetic macular edema (DME).

Commercial Platforms Bringing AI to the Clinic

Several commercial AI-OCT platforms are now making these capabilities accessible to clinicians, but here we will only highlight two leading FDA cleared platforms:

• Altris AI is an FDA cleared decision‑support browser-based platform that supports OCT data from all major manufacturers and accepts multiple file formats (DICOM, JPEG, PNG), making integration into existing devices easier. It identifies over 70 retinal pathologies, quantifies glaucoma-related biomarkers and provides clinician-friendly reports, complete with heatmaps to visually highlight abnormal regions.
• RetinAI Discovery is an FDA cleared image‑and‑data management platform that offers both clinical and research modules. Clinically, it analyzes OCT and fundus images, providing segmentation and quantification of fluid, retinal layers and pigment epithelial detachments. For researchers, Discovery CORE enables multi-site collaboration, automated grading and real-world evidence generation.

These tools are not just theoretical; they are actively improving clinical workflow. By highlighting subtle findings, quantifying changes and providing longitudinal analytics, AI platforms reduce human oversight, save time and allow clinicians to focus on patient-centered care rather than purely image review. Additional platforms exist, such as RetInSight (Topcon Healthcare), but do not yet have FDA clearance.

Clinical Relevance

Integrating AI into routine OCT interpretation is a game changer, notably because it will allow for:

• Enhanced accuracy: AI reduces variability in scan interpretation, helping clinicians detect conditions earlier, even in complex cases with multiple coexisting pathologies.
• Streamlined workflow: Automated segmentation, flagging of abnormal scans and longitudinal analysis reduce the time clinicians spend reviewing images, which is particularly valuable in high-volume practices.
• Patient engagement: Visualizations from AI platforms, such as heatmaps, progression graphs and quantitative metrics, can be shared with patients to explain disease state and treatment rationale.
• Research applications: Platforms like RetinAI CORE allow multi-center collaborations, standardized annotation and the generation of real-world evidence, bridging clinical care and research.

Limitations and Considerations

Despite their promise, AI-OCT tools have limitations including but not limited to:

• Data generalizability: Models trained on one population or device may underperform in others. Local validation is essential.
• Interpretability: While heatmaps and overlays help, some models remain “black boxes,” requiring clinician oversight.
• Integration: Incorporating AI into EMRs, PACS, or clinic workflows requires IT investment and staff training.
• Regulatory and cost considerations: FDA clearance, CE marking and other regulatory approvals vary, and licensing fees or cloud infrastructure may limit accessibility.

Final Thoughts

In case you forgot, AI is an augmentation, not a replacement. It complements, rather than replaces, human judgment, serving as a second pair of eyes to catch subtle pathology and provide quantitative support for clinical decisions. It can do it seamlessly and quickly. No more texting a friend an OCT image and waiting on a response.

Furthermore, AI doesn’t treat patients, integrate systemic history, consider patient preferences, or make final clinical judgments. Only clinicians can do that. As the volume of imaging continues to increase and as diseases become more complex, the combination of human expertise and AI-enhanced interpretation will define the next era in retina care.

To finish where we started, AI in ocular imaging acts much like that spelling and grammar checker, quietly assisting, highlighting, confirming and strengthening our work. It ensures that what we see is accurate, complete and as insightful as possible. And in a field where vision is everything (literally), that clarity is invaluable.

References

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