Noninvasive Coronary Artery Disease Detection Using Retinal Images: A Multimodal Study.

ABSTRACT:

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 fo

SUBMITTER: Li X 

PROVIDER: S-EPMC12670101 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature