Deep Learning for Diagnosis of Choroideremia and USH2A-Associated Rod-Cone Dystrophy Using Macular OCT Volumes.

OBJECTIVE

To develop and validate deep learning models to differentiate choroideremia (CHM), USH2A-associated rod-cone dystrophy, and healthy controls using macular OCT volumes.

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 CHM, 96 had USH2A-associated disease, and 67 were healthy controls.

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 CHM, 29 USH2A, 18 controls), all models achieved high performance (accuracy ≥0.963). The MoE achieved the highest accuracy on the internal test set (accuracy 0.988; 79/80; weighted F1-score 0.988), followed by CNN + vote (accuracy 0.975; 78/80; weighted F1-score 0.975) and the hybrid CNN-Transformer (accuracy 0.963; 77/80; weighted F1-score 0.962). Ophthalmologists achieved lower accuracy (0.875 and 0.863). External validation on an independent Rennes University Hospital data set (n = 28 volumes; CHM = 6, controls = 10, USH2A = 12), processed identically without retraining, yielded accuracy 28/28 for CNN + vote, whereas the MoE and hybrid CNN-Transformer each achieved 26/28.

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.