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Background <p>Coronary heart disease (CHD) is a major cause of mortality worldwide. This study aimed to develop and validate a multimodal deep learning algorithm using retinal imaging to assist in CHD risk assessment.</p> Methods <p>In this retrospective study, we developed a deep learning algorithm that integrates retinal fundus photographs and optical coherence tomography (OCT) images. A clinical nomogram was also developed by combining the imaging-based predictions with clinical risk factors. Model performance was evaluated on internal validation and test cohort using receiver operating characteristic (ROC) analysis and calibration curves.</p> Results <p>The algorithm was developed and validated using a dataset of 505 patients, which included 282 with CHD. On the training cohort, the model achieved an area under the curve (AUC) of 0.9954 (95% CI: 0.9904–1.0000). On the independent validation and test cohorts, the model achieved AUCs of 0.9834 (95% CI: 0.9556–1.0000) and 0.9138 (95% CI: 0.8404–0.9871), respectively. The nomogram demonstrated an AUC of 0.9963 (95% CI, 0.9923–1.0000) on the training cohort, and AUCs of 0.9423 (95% CI, 0.8626–1.0000) and 0.9153 (95% CI, 0.8289–1.0000) on the internal validation and test cohort, respectively.</p> Conclusions <p>This study demonstrates the feasibility of using a deep learning algorithm based on retinal imaging for CHD risk stratification. Future prospective, multicenter studies are needed to validate these findings and evaluate their potential clinical utility.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Ran Yan,
  • Xiaoxiao Guo,
  • Jiang Zhu,
  • Yan Zhu,
  • Tingting Hong,
  • Weijie Zhang,
  • Zongqing Ma,
  • Xinxiao Gao

摘要

Background

Coronary heart disease (CHD) is a major cause of mortality worldwide. This study aimed to develop and validate a multimodal deep learning algorithm using retinal imaging to assist in CHD risk assessment.

Methods

In this retrospective study, we developed a deep learning algorithm that integrates retinal fundus photographs and optical coherence tomography (OCT) images. A clinical nomogram was also developed by combining the imaging-based predictions with clinical risk factors. Model performance was evaluated on internal validation and test cohort using receiver operating characteristic (ROC) analysis and calibration curves.

Results

The algorithm was developed and validated using a dataset of 505 patients, which included 282 with CHD. On the training cohort, the model achieved an area under the curve (AUC) of 0.9954 (95% CI: 0.9904–1.0000). On the independent validation and test cohorts, the model achieved AUCs of 0.9834 (95% CI: 0.9556–1.0000) and 0.9138 (95% CI: 0.8404–0.9871), respectively. The nomogram demonstrated an AUC of 0.9963 (95% CI, 0.9923–1.0000) on the training cohort, and AUCs of 0.9423 (95% CI, 0.8626–1.0000) and 0.9153 (95% CI, 0.8289–1.0000) on the internal validation and test cohort, respectively.

Conclusions

This study demonstrates the feasibility of using a deep learning algorithm based on retinal imaging for CHD risk stratification. Future prospective, multicenter studies are needed to validate these findings and evaluate their potential clinical utility.