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

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

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 lab...

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Main Authors: Van Nguyen, MD, Sreenidhi Iyengar, Haroon Rasheed, MD, Galo Apolo, Zhiwei Li, Aniket Kumar, Hong Nguyen, Austin Bohner, MD, Kyle Bolo, MD, Rahul Dhodapkar, MD, Jiun Do, MD, PhD, Andrew T. Duong, MD, Jeffrey Gluckstein, MD, Kendra Hong, MD, Lucas L. Humayun, Alanna James, MD, Junhui Lee, MD, Kent Nguyen, OD, Brandon J. Wong, MD, Jose-Luis Ambite, PhD, Carl Kesselman, PhD, Lauren P. Daskivich, MD, Michael Pazzani, PhD, Benjamin Y. Xu, MD, PhD
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Ophthalmology Science
Subjects:
Artificial intelligence
Deep learning
Glaucoma
Screening
Telemedicine
Online Access:http://www.sciencedirect.com/science/article/pii/S2666914525000491
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