Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs.

Purpose: To compare the performance of a foundation model and a supervised learning-based model for detecting referable glaucoma from fundus photographs. Design: Evaluation of diagnostic technology. Participants: 6,116 participants from the Los Angeles County Department of Health Services Teleretinal Screening Program. Methods: Fundus photographs were labeled for referable glaucoma (cup-to-disc ratio ≥ 0.6) by certified optometrists. Four deep learning models were trained on cropped and uncropped images (Training N = 8,996; Validation N = 3,002) using two architectures: a vision transformer with self-supervised pretraining on fundus photographs (RETFound) and a convolutional neural network (VGG-19). Models were evaluated on a held-out test set (N = 1,000) labeled by glaucoma specialists and an external test set (N = 300) from University of Southern California clinics. Performance was assessed while varying training set size and stratifying by demographic factors. xRAI was used for saliency mapping. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC-ROC) and threshold-specific metrics. Results: The cropped image VGG-19 model achieved the highest AUC-ROC (0.924 [0.907-0.940]), which was comparable ( Conclusion: While both RETFound and VGG-19 models performed well for classification of referable glaucoma, foundation models may be preferable when training data is limited and when domain shift is expected. Training models using images cropped to the region of the optic nerve improves performance regardless of architecture but may reduce model generalizability.