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

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 b

SUBMITTER: Bolo K 

PROVIDER: S-EPMC12393662 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature