Deep Learning for Diagnosis of Choroideremia and

Objective: To develop and validate deep learning models to differentiate choroideremia (CHM), Design: Retrospective diagnostic study with external validation. Subjects: Two hundred sixty-two participants contributed 535 macular OCT volumes acquired on a Heidelberg Spectralis (10 165 B-scans): 99 participants had Methods: Volumes acquired between 2012 and 2021 were standardized to 19 B-scans and split at the patient level into training (76%), validation (12%), and test (12%) sets. Three strategies were compared: (1) a slice-based convolutional neural network (CNN) (ResNet-50) with majority-vote aggregation; (2) a Mixture-of-Experts (MoE) model with learned slice weighting; and (3) a hybrid CNN-Transformer integrating per-slice features across the volume. Two ophthalmologists masked to clinical and genetic data graded the 80 test volumes. Main Outcome Measures: Volume-level accuracy and weighted F1-score. Results: On the independent test set (80 volumes: 33 Conclusions: Deep learning applied to macular OCT volumes accurately distinguished CHM, USH2A-associated rod-cone dystrophy, and healthy controls, and achieved higher observed accuracy than masked human readers in this OCT-only setting. Both learned slice weighting and simple aggregation achieved strong performance, suggesting that increased architectural complexity may not be necessary for this 3-class classification task. Further validation in larger, more diverse, and clinically representative cohorts is required to determine the potential role of such models as complementary diagnostic support. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.