Artificial Intelligence‐Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs

Artificial Intelligence‐Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs

Background Prompt diagnosis of acute central retinal artery occlusion (CRAO) is crucial for therapeutic management and stroke prevention. However, most stroke centers lack onsite ophthalmic expertise before considering fibrinolytic treatment. This study aimed to develop, train, and test a deep learn...

Full description

Saved in:
Bibliographic Details
Main Authors: Ayse Gungor, Ilias Sarbout, Aubrey L. Gilbert, Steffen Hamann, Pierre Lebranchu, Cristina Hobeanu, Philippe Gohier, Catherine Vignal‐Clermont, Oana M. Dumitrascu, Salomon‐Yves Cohen, Wolf A. Lagrèze, Nicolas Feltgen, Frank van der Heide, Cédric Lamirel, Jost B. Jonas, Michael Obadia, Daniel Racoceanu, Dan Milea
Format: Article
Language:English
Published: Wiley 2025-07-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
acute stroke
artificial intelligence
central retinal artery occlusion
cerebrovascular stroke
early diagnosis
machine learning
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.124.041441
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background Prompt diagnosis of acute central retinal artery occlusion (CRAO) is crucial for therapeutic management and stroke prevention. However, most stroke centers lack onsite ophthalmic expertise before considering fibrinolytic treatment. This study aimed to develop, train, and test a deep learning system to detect hyperacute CRAO on retinal fundus photographs within the critical 4.5‐hour treatment window and up to 24 hours after visual loss to aid in secondary stroke prevention. Methods Our retrospective, cross‐sectional study included 1322 color fundus photographs from 771 patients with acute visual loss due to CRAO, central retinal vein occlusion, nonarteritic anterior ischemic optic neuropathy, and healthy controls. Photographs were collected from 9 expert neuro‐ophthalmology centers in 6 countries, including 3 randomized clinical trials. Training included 1039 photographs (517 patients), followed by testing on 2 data sets: (1) hyperacute CRAO (54 photographs, 54 patients) and (2) CRAO within 24 hours after visual loss (110 photographs, 109 patients). Results The deep learning system achieved an area under the receiver operating characteristic curve of 0.96 (95% confidence interval (CI), 0.95–0.98), a sensitivity of 92.6% (95% CI, 87.0–98.0), and a specificity of 85.0% (95% CI, 81.8–92.8) for detecting CRAO at hyperacute stage, with similar results within 24 hours. The deep learning system outperformed stroke neurologists on a subset of hyperacute testing data set (120 photographs, 120 patients). Conclusions A deep learning system can accurately detect hyperacute CRAO on retinal photographs within a time window compatible with urgent fibrinolysis. If further validated, such systems could improve patient selection for fibrinolytic trials and optimize secondary stroke prevention. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT06390579.
ISSN:2047-9980

Similar Items