AUTHOR=Siraz Sadia , Kamanda Hindolo , Gholami Sina , Nabil Ahammed Sakir , Rao Nitya Tangada , Ong Sally S. , Alam Minhaj Nur 
  
TITLE=Multi-class classification of central and non-central geographic atrophy using optical coherence tomography
  
JOURNAL=Experimental Biology and Medicine
  
VOLUME=Volume 251 - 2026
  
YEAR=2026
  
URL=https://www.ebm-journal.org/journals/experimental-biology-and-medicine/articles/10.3389/ebm.2026.11213
  
DOI=10.3389/ebm.2026.11213
  
ISSN=1535-3699
  
ABSTRACT=This retrospective study aimed 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. A total of 455 OCT volumes from 91 patients at Wake Forest University School of Medicine were evaluated with 258 Central GA (CGA), 74 Non-Central GA (NCGA), and 123 no GA (NGA) cases. For GA classification, the dataset was supplemented with early and intermediate non-neovascular AMD and neovascular AMD cases, which were sourced from four public repositories. Two experimental approaches were tested: utilizing all B-scans within each OCT volume and selectively using B-scans containing foveal regions. Models were trained using transfer learning with patient-level data splitting at a 70:15:15 ratio, and performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), F1 score, and accuracy as primary outcome measures. Vision Transformers (ViTs) demonstrated the most consistent performance across the evaluated architectures. For CGA versus NCGA classification, ViT-B/16 achieved a B-scan-level AUC-ROC of 0.728 ± 0.058 in Case 2, while volume/visit-level AUC-ROC was 0.837 ± 0.011 in Case 1 and 0.656 ± 0.070 in Case 2. NCGA recognition remained challenging, particularly in Case 2. In contrast, GA-versus-NGA and GA-versus-non-GA AMD classification showed substantially stronger discrimination. These findings support the feasibility of OCT-based automated GA phenotyping while highlighting NCGA recognition as an important area for further optimization and external validation.