BMC medical imaging · 2026
A deep learning algorithm for coronary heart disease prediction based on retinal fundus photographs and optical coherence tomography.
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Yan Ran, Guo Xiaoxiao, Zhu Jiang, Zhu Yan, Hong Tingting, Zhang Weijie, Ma Zongqing, Gao Xinxiao
Аннотация
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.
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.