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A deep learning algorithm for coronary heart disease prediction based on retinal fundus photographs and optical

Ran Yan1, Xiaoxiao Guo1, Jiang Zhu2

  • 1Department of Ophthalmology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.

BMC Medical Imaging
|July 10, 2026
PubMed

Insights

A new deep learning algorithm using retinal images can help assess coronary heart disease (CHD) risk. This AI tool shows high accuracy in predicting CHD, offering a promising approach for early detection and management.

Area of Science:

  • Ophthalmology
  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Coronary heart disease (CHD) represents a significant global health burden, contributing to widespread mortality.
  • Current CHD risk assessment methods can be enhanced by novel, non-invasive techniques.
  • Retinal imaging offers a unique window into systemic vascular health, potentially reflecting cardiovascular status.

Purpose of the Study:

  • To develop and validate a multimodal deep learning algorithm for coronary heart disease (CHD) risk stratification.
  • To integrate retinal fundus photographs and optical coherence tomography (OCT) images for enhanced predictive power.
  • To create a clinical nomogram combining imaging-derived predictions with established clinical risk factors.

Main Methods:

  • A retrospective study involving 505 patients (282 with CHD) utilizing retinal fundus photographs and OCT images.
  • Development of a deep learning algorithm integrating multimodal retinal imaging data.
  • Construction of a clinical nomogram incorporating imaging predictions and clinical risk factors; model performance assessed via ROC analysis and calibration curves.

Main Results:

  • The deep learning algorithm achieved high performance, with Area Under the Curve (AUC) values of 0.9954 (training), 0.9834 (validation), and 0.9138 (test cohort).
  • The integrated clinical nomogram demonstrated strong predictive accuracy, achieving AUCs of 0.9963 (training), 0.9423 (validation), and 0.9153 (test cohort).
  • The algorithm and nomogram showed robust performance across different patient cohorts, indicating reliable CHD risk stratification capabilities.

Conclusions:

  • Deep learning analysis of retinal imaging is a feasible approach for coronary heart disease (CHD) risk stratification.
  • The developed algorithm and nomogram show significant potential for assisting in clinical CHD risk assessment.
  • Further prospective, multicenter validation is warranted to confirm clinical utility and widespread applicability.
Abstract

Keywords:
Multimodal model, Deep learning, Coronary heart disease, Retinal fundus photograph, Optical coherence tomography

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