PurposeTo evaluate RETFound, a foundation AI model, using a diverse clinical research dataset to assess its accuracy in detecting glaucoma using optic disc photographs. The model's accuracy for glaucoma detection was evaluated across race, age, glaucoma severity and various training cycles (epochs) and dataset sample sizes. DesignEvaluation of a diagnostic technology Subjects, Participants, and ControlsThe study included 9787 color fundus photographs (CFP) from 2329 participants of diverse race (White (73.3%), Black (13.6%) and other (13%) , disease severity (21.8% mild glaucoma, 7.2% moderate or advanced glaucoma, 60.3% not glaucoma, and 10.7% unreported), and age (48.8% <60 years, 51.1% > 60 years) from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES). All fundus photographs were graded as "Glaucomatous" or "Non-glaucomatous". MethodsThe study employed RETFound, a self-supervised learning model, to perform binary glaucoma classification. The diagnostic accuracy of RETFound was iteratively tested across different combinations of dataset sample sizes (50 to 2000 optic disc photographs), training cycles (5 to 50), and study subpopulations stratified by severity of glaucoma, age and race). Main Outcome MeasuresDiagnostic Accuracy (AUC) for classifying CFP as "Glaucomatous" or "Non-glaucomatous". ResultsPerformance increased with larger training datasets and more training cycles, improving from50 training images and 5 epochs (AUC: 0.52) to 2,000 training images and 50 epochs (AUC: 0.86), with reduced gain in performance from approximately 500 and 1000 training images (AUC of 0.82 and 0.83, respectively). Performance was consistent across race and age for all training size and cycle number combinations: Black (AUC=0.87) vs other (AUC=0.86), and >60 years (AUC=0.84) vs <60 years (AUC=0.87). Performance was significantly higher in patients with moderate to severe vs mild glaucoma (AUC=0.95 vs 0.84, respectively). ConclusionsGood RETFound performance was observed with a relatively small sample size of optic disc photographs used for fine tuning and across differences in race and age. RETFound’s ability to adapt across a range of CFP training conditions and populations suggests it is a promising tool to automate glaucoma detection in a variety of use cases.