Abstract

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 (p = 0.07) to the cropped image RETFound model (0.911 [0.892-0.930]), which achieved the highest Youden-optimal performance (sensitivity 82.6%, specificity 88.2%) and F1 score (0.801). Cropped image models outperformed their uncropped counterparts within each architecture (p < 0.001 for AUC-ROC comparisons). RETFound models had a performance advantage when …
Purpose

Design

Participants

Methods

Main Outcome Measures

Results

Date

January 23, 2026

Authors

Kyle Bolo, Tran Huy Nguyen, Sreenidhi Iyengar, Zhiwei Li, Van Nguyen, Brandon Wong, Jiun Do, Jose-Luis Ambite, Carl Kesselman, Lauren Daskivich, Benjamin Xu

Journal

medRxiv

Pages

2025.08. 21.25334170

Publisher

Cold Spring Harbor Laboratory Press