Dataset Information

Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction.

ABSTRACT: Cardiovascular disease (CVD), the leading cause of death globally, is associated with complicated underlying risk factors. We develop an artificial intelligence model to identify CVD using multimodal data, including clinical risk factors and fundus photographs from the Samsung Medical Center (SMC) for development and internal validation and from the UK Biobank for external validation. The multimodal model achieves an area under the receiver operating characteristic curve (AUROC) of 0.781 (95% confidence interval [CI] 0.766-0.798) in the SMC and 0.872 (95% CI 0.857-0.886) in the UK Biobank. We further observe a significant association between the incidence of CVD and the predicted risk from at-risk patients in the UK Biobank (hazard ratio [HR] 6.28, 95% CI 4.72-8.34). We visualize the importance of individual features in photography and traditional risk factors. The results highlight that non-invasive fundus photography can be a possible predictive marker for CVD.

SUBMITTER: Lee YC

PROVIDER: S-EPMC9894867 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction.

Lee Yeong Chan YC Cha Jiho J Shim Injeong I Park Woong-Yang WY Kang Se Woong SW Lim Dong Hui DH Won Hong-Hee HH

NPJ digital medicine 20230202 1

Cardiovascular disease (CVD), the leading cause of death globally, is associated with complicated underlying risk factors. We develop an artificial intelligence model to identify CVD using multimodal data, including clinical risk factors and fundus photographs from the Samsung Medical Center (SMC) for development and internal validation and from the UK Biobank for external validation. The multimodal model achieves an area under the receiver operating characteristic curve (AUROC) of 0.781 (95% co ...[more]

PMID: 36732671

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Dataset Information

Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction.

Publications

Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction.

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