Evaluating a Foundation Artificial Intelligence Model for Glaucoma Detection Using Color Fundus Photographs.

ABSTRACT:

Purpose

To evaluate RETFound, a foundation artificial intelligence 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.

Design

Evaluation of a diagnostic technology.

Participants

The study included 9787 color fundus photographs (CFPs) from 2329 participants of diverse race (White [73.4%], 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 and the African

SUBMITTER: Chuter B 

PROVIDER: S-EPMC11625234 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature