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성균관대학교 삼성융합의과학원, 삼성서울병원

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npj Digit. Med., Published: 02 February 2023 | https://doi.org/10.1038/s41746-023-00748-4 Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction

Yeong Chan Lee^1,2,6, Jiho Cha^3,6, Injeong Shim¹, Woong-Yang Park⁴, Se Woong Kang⁵, Dong Hui Lim^1,5,7 and Hong-Hee Won^1,4,7

¹Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of
Korea.
²Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea.
³Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
⁴Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
⁵Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
⁶These authors contributed equally: Yeong Chan Lee, Jiho Cha.
⁷These authors jointly supervised this work: Dong Hui Lim, Hong-Hee Won.

Corresponding authors: Correspondence to Dong Hui Lim or Hong-Hee Won.

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.

논문정보

형식Research article
게재일2023년 02월 (BRIC 등록일 2023-02-14)
연구진국내 연구진
분야 의약학 > 유전 및 유전체의학

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