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使用视网膜图像进行非侵入性冠状动脉疾病检测:一项多模式研究

Xiaohui Li¹, Xiaoyu Dong², Leilei Chen²

¹School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.

|November 20, 2025

概括

使用视网膜图像的深度学习显示出检测冠状动脉疾病 (CAD) 的前景. 这种基于人工智能的方法提供了一种非侵入性的风险分层方法,有可能改善患者的治疗结果.

科学领域:

眼科和心脏病学眼科和心脏病学
医疗保健中的人工智能
医学成像分析医学成像分析

背景情况:

冠状动脉疾病 (CAD) 是一个主要的全球健康问题.
目前的CAD检测方法在安全性和适用性方面存在局限性.
对于CAD来说,急需安全的,非侵入性的诊断工具.

研究的目的:

开发用于使用视网膜图像进行CAD检测的深度学习框架.
创建冠状动脉疾病的视觉和多式检测系统.
探索视网膜血管在CAD诊断中的实用性.

主要方法:

一项多中心横截面研究涉及383名接受冠状动脉血管造影的患者.
开发了三种人工智能模型:视网膜图像CNN,临床指标MLP和多式联接模型.
利用交叉模式的注意力机制来整合视觉和临床数据.

主要成果:

仅视网膜图像模型的AUC为0.80,超过中等风险患者的临床评分.
综合视网膜图像和临床数据的多式模式显示出卓越的性能.
最好的多式联络模型实现了0.91的AUC,准确率为87.0%,灵敏度为92.1%.

结论:

视网膜图像和血管系统包含用于非侵入性CAD检测的宝贵信息.
视网膜成像为新的诊断工具提供了一个有希望的,安全的临床风险因素.
基于AI的 fundus成像可以帮助冠心病的风险分层.

关键词:

冠状动脉疾病是一种冠状动脉疾病.多式联络功能融合是多式联络的功能.这是一种非侵入性的方法.视网膜成像视网膜成像

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