Purpose: To develop and validate deep learning (DL)-based models for classifying geographic atrophy (GA) subtypes using Optical Coherence Tomography (OCT) scans across four clinical classification tasks. Design: Retrospective comparative study evaluating three DL architectures on OCT data with two experimental approaches. Subjects: 455 OCT volumes (258 Central GA [CGA], 74 Non-Central GA [NCGA], 123 no GA [NGA]) from 104 patients at Atrium Health Wake Forest Baptist. For GA versus age-related macular degeneration (AMD) classification, we supplemented our dataset with AMD cases from four public repositories. Methods: We implemented ResNet50, MobileNetV2, and Vision Transformer (ViT-B/16) architectures using two approaches: (1) utilizing all B-scans within each OCT volume and (2) selectively using B-scans containing foveal regions. Models were trained using transfer learning, standardized data augmentation, and patient-level data splitting (70:15:15 ratio) for training, validation, and testing. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC-ROC), F1 score, and accuracy for each classification task (CGA vs. NCGA, CGA vs. NCGA vs. NGA, GA vs. NGA, and GA vs. other forms of AMD). Results: ViT-B/16 consistently outperformed other architectures across all classification tasks. For CGA versus NCGA classification, ViT-B/16 achieved an AUC-ROC of 0.728 and accuracy of 0.831 using selective B-scans. In GA versus NGA classification, ViT-B/16 attained an AUC-ROC of 0.950 and accuracy of 0.873 with selective B-scans. All models demonstrated exceptional performance in distinguishing GA from other AMD forms (AUC-ROC>0.998). For multi-class classification, ViT-B/16 achieved an AUC-ROC of 0.873 and accuracy of 0.751 using selective B-scans. Conclusions: Our DL approach successfully classifies GA subtypes with clinically relevant accuracy. ViT-B/16 demonstrates superior performance due to its ability to capture spatial relationships between atrophic regions and the foveal center. Focusing on B-scans containing foveal regions improved diagnostic accuracy while reducing computational requirements, better aligning with clinical practice workflows.

The authors have declared no competing interest.

The study was funded by NEI R21EY035271 (MNA), R15EY035804 (MNA); and UNC Charlotte Faculty Research Grant (MNA).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Institutional Review Board of Atrium Health Wake Forest Baptist gave ethical approval for this work

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All data produced in the present study are available upon reasonable request to the authors