Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis.

Develop a machine learning model that predicts early-onset AMD based on early patient comorbidities DESIGN: Retrospective case-control study. This study used 2 datasets: a tertiary eye care center dataset (930 AMD patients, 392 early-onset, 538 late-onset) and the All of Us dataset for validation (560-580 AMD patients early-onset, 560-580 matched controls without AMD). Health records of AMD patients from a tertiary hospital (1999-2023) and the All of Us Research Program, a nationwide longitudinal cohort of U.S. adults (data collected 6/2016-2/2025) were collected. Unsupervised clustering using Uniform Manifold Approximation and Projection (UMAP) was performed on the tertiary eye care center dataset to identify distinct patient subgroups. Supervised machine learning models, including gradient-boosted decision trees (GBDT), logistic regression, and random forests, were trained to predict early-onset AMD. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while feature importance was assessed using the Gini index for tree-based models and coefficient magnitudes for logistic regression. Additionally, odds ratios for comorbidities associated with early-onset AMD were estimated using 2 × 2 contingency tables in the validation dataset (All of Us). Clustering identified 2 distinct patient groups, with 1 (93.08% early-onset AMD dominated) showing high rates of inflammatory joint disorders, hypertension, and dyslipidemia. Multiple supervised machine learning models achieved ∼76% accuracy in predicting early-onset AMD from early comorbidities (diagnosed before age of 55). Like clustering, analysis of supervised machine learning models identified inflammatory joint disorders, hypertension, and dyslipidemia as the most predictive features. These comorbidities were validated using the All of Us dataset, with essential hypertension (OR: 5.80, 95% CI: [4.25-7.93], P < .05), rheumatoid arthritis (OR: 4.61, 95% CI: [1.55-13.72], P < .05), and hyperlipidemia (OR: 5.16, 95% CI: [3.76-7.08], P < .05) showing strong associations with early-onset AMD. Screening hypertension, dyslipidemia, and inflammatory joint disorders in ophthalmologic evaluations may enable earlier AMD detection and intervention.

Purpose: To validate a deep learning algorithm for automated segmentation and quantitative assessment of the ellipsoid zone (EZ) and RPE–Bruch’s membrane (BM) complex in healthy and geographic atrophy (GA) eyes. Methods: In this retrospective study, SD-OCT volume scans from 30 healthy and 30 eyes with GA were analysed. NMI-Outer Retina Analyzer was used to segment the inner EZ, inner RPE, and outer BM. Average thicknesses of EZ-RPE, EZ-BM, and RPE-BM were calculated from volumes and across nine ETDRS sectors. Manual segmentations were corrected by two masked expert graders and were compared using ICC. Dice coefficients (DC), Pearson correlation, and absolute thickness differences were used to assess agreement between automated and manual segmentation. Heat maps were generated to visualize thicknesses. Results: Thirty healthy eyes and thirty GA eyes were included in the analysis. Mean EZ–RPE, EZ–BM, and RPE–BM thicknesses were 47.55 ± 6.75 µm, 69.49 ± 6.92 µm, and 21.94 ± 3.46 µm, in the healthy eyes and 15.65 ± 11.09 µm, 39.18 ± 23.28 µm, and 23.52 ± 16.21 µm in GA eyes respectively. The model demonstrated high segmentation accuracy, with mean DC of 0.998 in healthy eyes and 0.995–0.998 in GA eyes. In healthy eyes, differences between automated and manual measurements were minimal (1.42 ± 3.39 μm (2.98%) for EZ–RPE, 1.31 ± 3.18 μm (1.88%) for EZ–BM, and 0.67 ± 1.71 μm (3.05%) for RPE–BM) which is within 1.88-3.05% from the gold standard (manual corrections), whereas GA eyes showed greater variability (mean differences of 3.61 ± 8.62 μm (23.06%) for EZ–RPE, 4.28 ± 11.34 μm (10.92%) for EZ–BM, and 4.4 ± 10.45 μm (18.71%) for RPE–BM). Heat maps revealed increased variability at the junctional zone surrounding atrophy. Automated and manual measurements showed strong correlations across all sectors in GA eyes (r = 0.97 for EZ–BM, 0.96 for EZ–RPE, and 0.89 for RPE–BM). Conclusions: The NMI-ORA enables accurate, automated segmentation and quantification of outer retinal layers, with performance comparable to expert graders.

Purpose: This study aimed to validate a deep learning algorithm for automated segmentation and quantitative assessment of the ellipsoid zone (EZ) and the retinal pigment epithelium (RPE)–Bruch’s membrane (BM) complex in healthy eyes and geographic atrophy (GA) eyes. Methods: In this retrospective study, spectral-domain optical coherence tomography (SD-OCT) volume scans from 30 healthy eyes and 30 eyes with GA were analyzed. An NMI-Outer Retina Analyzer was used to segment the inner EZ, inner RPE, and outer BM. Average thicknesses of the EZ-RPE, EZ-BM, and RPE-BM were calculated from volumes and across nine ETDRS sectors. Manual segmentations were corrected by two masked expert graders and compared using intraclass correlation coefficients (ICCs). Dice coefficients (DCs), Pearson correlation coefficients, and absolute thickness differences were used to assess agreement between automated and manual segmentation. Heat maps were generated to visualize thickness. Results: Thirty healthy eyes and thirty GA eyes were included in the analysis. Mean EZ-RPE, EZ-BM, and RPE-BM thicknesses were 47.55 ± 6.75 µm, 69.49 ± 6.92 µm, and 21.94 ± 3.46 µm, respectively, in the healthy eyes and 15.65 ± 11.09 µm, 39.18 ± 23.28 µm, and 23.52 ± 16.21 µm, respectively, in GA eyes. The model demonstrated high segmentation accuracy, with a mean DC of 0.998 in healthy eyes and 0.995–0.998 in GA eyes. In healthy eyes, differences between automated and manual measurements were minimal (1.42 ± 3.39 μm (2.98%) for EZ-RPE, 1.31 ± 3.18 μm (1.88%) for EZ-BM, and 0.67 ± 1.71 μm (3.05%) for RPE-BM), all within 1.88–3.05% of the gold standard (manual corrections). In contrast, GA eyes showed greater variability (mean differences of 3.61 ± 8.62 μm (23.06%) for EZ-RPE, 4.28 ± 11.34 μm (10.92%) for EZ-BM, and 4.4 ± 10.45 μm (18.71%) for RPE-BM). Heat maps revealed increased variability in the junctional zone surrounding the atrophy. Automated and manual measurements showed strong correlations across all sectors in GA eyes (r = 0.97 for EZ-BM, 0.96 for EZ-RPE, and 0.89 for RPE-BM). Conclusions: The NMI-ORA enables accurate, automated segmentation and quantification of outer retinal layers, with performance comparable to that of expert graders.

Objectives: To validate a deep learning-based algorithm for automated detection, segmentation, and quantitative assessment of retinal fluid on optical coherence tomography (OCT) across multiple retinal diseases. Methods: In this retrospective validation study, spectral-domain OCT volume scans from eyes with neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), and macular telangiectasia type 2 (MacTel) were analyzed. Automated segmentation was performed using the fluid analyzer on Skopix by NetraMind Innovations, a deep learning-based framework trained to identify intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective material (SHRM). Automated segmentations were reviewed and manually corrected by two masked experienced retina graders, with discrepancies adjudicated by a senior retina specialist. Scan-level detection performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC), and Gwet’s first-order agreement coefficient (AC1). Spatial segmentation agreement across all B-scans was assessed using the Dice coefficient. Quantitative agreement between automated and manually corrected fluid volumes was evaluated using correlation and Bland–Altman analyses. Inter-grader reliability was assessed using the intraclass correlation coefficient (ICC). Results: A total of 98 OCT volumes comprising 31 nAMD, 25 DME, and 42 MacTel eyes were included. In nAMD, IRF detection demonstrated a sensitivity of 81.8%, specificity of 83.0%, and AUC of 0.876, while SRF demonstrated a sensitivity of 81.4%, specificity of 96.7%, and AUC of 0.901. In DME, sensitivity, specificity, and AUC were 99.6%, 97.3%, and 0.989 for IRF and 98.4%, 96.4%, and 0.978 for SRF, respectively. In MacTel, IRF demonstrated a sensitivity of 87.3%, specificity of 95.1%, and AUC of 0.927, while SRF demonstrated a sensitivity of 86.8%, specificity of 98.4%, and AUC of 0.929. Segmentation agreement between automated and manually corrected annotations was high across disease groups, with Dice coefficients of 0.829 and 0.922 for IRF and SRF in nAMD, 0.858 and 0.941 in DME, and 0.908 and 0.97 in MacTel, respectively. Automated fluid volumes demonstrated strong correlations with manually corrected measurements, with minimal systematic bias on Bland–Altman analysis. Conclusions: The automated segmentation algorithm demonstrated strong validation performance for the detection and quantitative assessment of retinal fluid across nAMD, DME, and MacTel. High segmentation agreement, particularly for IRF and SRF, and strong volumetric agreement with expert-corrected measurements support the potential use of automated OCT fluid quantification in clinical practice and research.