Article

Expert-Level Detection of Referable Glaucoma from Fundus Photographs in a Safety Net Population: The AI and Teleophthalmology in Los Angeles Initiative

2024-08-26

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Purpose</h4> To develop and test a deep learning (DL) algorithm for detecting referable glaucoma in the Los Angeles County (LAC) Department of Health Services (DHS) teleretinal screening program. <h4>Methods</h4> Fundus photographs and patient-level labels of referable glaucoma (defined as cup-to-disc ratio [CDR] ≥ 0.6) provided by 21 trained optometrist graders were obtained from the LAC...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 79faaea8-d745-51b6-bca3-ac2f0e921369
DOI: 10.1101/2024.08.25.24312563

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Expert-Level Detection of Referable Glaucoma from Fundus Photographs in a Safety Net Population: The AI and Teleophthalmology in Los Angeles InitiativeDOI 10.1101/2024.08.25.24312563Read

Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus PhotographsSemantic proximity Read

Enhancing Glaucoma Detection through Supervised Pre-training with Intermediate Phenotypes: A Multi-Institutional StudySemantic proximity Read

A generalized computer vision model for improved glaucoma screening using fundus imagesSemantic proximity Read

Glaucoma Detection Using Deep Learning and Prompt-Based Explainable Report GenerationSemantic proximity Read

Autonomous Screening for Laser Photocoagulation in Fundus Images Using Deep LearningSemantic proximity Read

Deriving OCT-Equivalent Retinal Nerve Fiber Layer Thickness Maps from Fundus Photographs with Deep Learning Improves Glaucoma DiagnosisSemantic proximity Read

A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus PhotographsSemantic proximity Read

Applications of Artificial Intelligence in Optometric Diagnostics: A Review of Techniques and Clinical ImpactSemantic proximity Read

Datasheet for the IDHea Primary Eye Care Dataset: A Real-World Ocular Imaging Resource for ResearchSemantic proximity Read

How does AI detect diabetic retinopathy from retinal photos? A heatmap analysis of 54 deep learning modelsSemantic proximity Read

EyeDx: A Scalable and Interpretable Multitask Deep Learning Framework for Comprehensive Ocular Disease Detection in Fundus ImagesSemantic proximity Read

Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Mexico: Independent and Assistive use CasesSemantic proximity Read