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

Abstract

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.

Keywords: acute stroke; artificial intelligence; central retinal artery occlusion; cerebrovascular stroke; early diagnosis; machine learning; visual loss.

Figure 1. Preprocessing of the color fundus photographs.

Original fundus photographs, taken with 2 different cameras in 2 patients with central retinal artery occlusion (top row). The bottom row represents the corresponding preprocessed images, after the custom background removal.

Figure 2. Superimposed and averaged disease‐specific class‐activation maps.

Superimposed and averaged class‐activation maps, revealing areas of interest with highest pixel activation to differentiate between (A) normal, (B) central retinal artery occlusion, (C) central retinal vein occlusion, and (D) nonarteritic anterior ischemic optic neuropathy.

Figure 3. Performance (accuracy, sensitivity, specificity) of the deep learning system and the 3 stroke neurologists in detecting hyperacute central retinal artery occlusion.

Comparison of the performance (accuracy, sensitivity, specificity) of the deep learning system to the performance of each stroke neurologist in detecting hyperacute CRAO from other differential diagnosis of sudden, painless visual loss (central retinal vein occlusion and nonarteritic anterior ischemic optic neuropathy) and from healthy controls on 120 color fundus photographs (30 cases with hyperacute CRAO and 90 cases without CRAO). CRAO indicates central retinal artery occlusion; DLS, deep learning system; and SN, stroke neurologist.