Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population.

ABSTRACT:

Purpose

Develop and test a deep learning (DL) algorithm for detecting referable glaucoma.

Design

Retrospective cohort study.

Participants

A total of 6116 patients from the Los Angeles County (LAC) Department of Health Services (DHS) were included.

Methods

Fundus photographs and patient-level labels of referable glaucoma (cup-to-disc ratio ≥0.6) provided by 21 certified optometrists. A DL algorithm based on the Visual Geometry Group-19 architecture was trained using patient-level labels generalized to images from both eyes. Area under the receiver operating curve (AUROC), sensitivity, and specificity were calculated to assess algorithm performance using an independent test set that was also graded by 13 clinicians with 0 to 10 years of experience. Algorithm perf

SUBMITTER: Nguyen V 

PROVIDER: S-EPMC11999268 | biostudies-literature | 2025 Jul-Aug

REPOSITORIES: biostudies-literature