ArticleOphthalmology science
Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population.
Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deriving OCT-Equivalent Retinal Nerve Fiber Layer Thickness Maps from Fundus Photographs with Deep Learning Improves Glaucoma Diagnosis.Ophthalmology science · 2026Article
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Comparison of RETFound and a Supervised Convolutional Neural Network for Detection of Referable Glaucoma from Fundus Photographs.Ophthalmology science · 2026Article
- Acute Angle Closure Incidence in a Large Countywide Safety Net Teleretinal Screening Program.JAMA ophthalmology · 2025Article
- Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.Journal of clinical medicine · 2025Review
- Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
24 authors.
Funding
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 performance was tested using reference labels provided by either LAC DHS optometrists or an expert panel of 3 glaucoma specialists. Main Outcome Measures: Area under the receiver operating curve, sensitivity, and specificity. Results: The DL algorithm was trained using 12 998 images from 5616 patients (2086 referable glaucoma, 3530 nonglaucoma). In this data set, the mean age was 56.8 ± 10.5 years with 54.8% women, 68.2% Latinos, 8.9% Blacks, 6.0% Asians, and 2.7% Whites. One thousand images from 500 patients (250 referable glaucoma, 250 nonglaucoma) with similar demographics ( Conclusions: A DL algorithm for detecting referable glaucoma trained using patient-level data provided by certified LAC DHS optometrists approximates or exceeds performance by ophthalmologists and optometrists, who exhibit variable sensitivity and specificity unrelated to experience level. Implementation of this algorithm in screening workflows could help reallocate resources and provide more reproducible and timely glaucoma care. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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