Noninvasive Coronary Artery Disease Detection Using Retinal Images

作者：Xiaohui Li, Xiao-yu Dong, Leilei Chen, Na Su, Kun Huang, Shuo Li, Yuchen Wen, Mingming Zhang, Bing Xu, Songtao Yuan, Junhong Wang, Qiang Chen · 发表于：JACC Advances · 年份：2025 · DOI：10.1016/j.jacadv.2025.102341 · 被引用次数：1 · 研究领域：Retinal Imaging and Analysis、Retinal and Optic Conditions、Artificial Intelligence in Healthcare

BACKGROUND: Coronary artery disease (CAD) is a leading cause of morbidity and mortality globally. However, current detection methods have various safety concerns and are not suitable for all populations. Exploring safe, noninvasive detection methods is crucial. OBJECTIVES: The objective of the study was to develop a deep learning-based visual and multimodal detection framework for CAD using retinal images. METHODS: We conducted a multicenter cross-sectional study including 383 patients who underwent successful coronary angiography between November 2022 and September 2024 at 4 hospitals. Three models were developed for CAD detection: a convolutional network-based model for retinal images, a hybrid model combining a medical large language model and multilayer perceptron for clinical indicators, and a multimodal model integrating both via a cross-modal attention mechanism. RESULTS: The visual algorithm trained solely on retinal images achieved an area under the receiver operating characteristic curve (AUC) of 0.80 (95% CI: 0.75-0.85), with 90.5% sensitivity and 59.6% specificity. Compared to the CAD consortium clinical score, it showed higher accuracy (76.2%) and sensitivity (87.4%) in the test group. Notably, in the intermediate-risk population (clinical score 15%-85%), it outperformed the clinical indicators-only model with higher AUC (0.79 vs 0.75), accuracy, and sensitivity. Multimodal models combining retinal images and clinical indicators further improved detection, with t...