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A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Publication ,  Journal Article
Cheung, CY; Xu, D; Cheng, C-Y; Sabanayagam, C; Tham, Y-C; Yu, M; Rim, TH; Chai, CY; Gopinath, B; Mitchell, P; Poulton, R; Moffitt, TE; Li, LJ ...
Published in: Nature biomedical engineering
June 2021

Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.

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Published In

Nature biomedical engineering

DOI

EISSN

2157-846X

ISSN

2157-846X

Publication Date

June 2021

Volume

5

Issue

6

Start / End Page

498 / 508

Related Subject Headings

  • Stroke
  • Risk Factors
  • Risk Assessment
  • Retrospective Studies
  • Retinal Vessels
  • Retina
  • Photography
  • Myocardial Infarction
  • Middle Aged
  • Male
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Cheung, C. Y., Xu, D., Cheng, C.-Y., Sabanayagam, C., Tham, Y.-C., Yu, M., … Wong, T. Y. (2021). A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nature Biomedical Engineering, 5(6), 498–508. https://doi.org/10.1038/s41551-020-00626-4
Cheung, Carol Y., Dejiang Xu, Ching-Yu Cheng, Charumathi Sabanayagam, Yih-Chung Tham, Marco Yu, Tyler Hyungtaek Rim, et al. “A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.Nature Biomedical Engineering 5, no. 6 (June 2021): 498–508. https://doi.org/10.1038/s41551-020-00626-4.
Cheung CY, Xu D, Cheng C-Y, Sabanayagam C, Tham Y-C, Yu M, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nature biomedical engineering. 2021 Jun;5(6):498–508.
Cheung, Carol Y., et al. “A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.Nature Biomedical Engineering, vol. 5, no. 6, June 2021, pp. 498–508. Epmc, doi:10.1038/s41551-020-00626-4.
Cheung CY, Xu D, Cheng C-Y, Sabanayagam C, Tham Y-C, Yu M, Rim TH, Chai CY, Gopinath B, Mitchell P, Poulton R, Moffitt TE, Caspi A, Yam JC, Tham CC, Jonas JB, Wang YX, Song SJ, Burrell LM, Farouque O, Li LJ, Tan G, Ting DSW, Hsu W, Lee ML, Wong TY. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nature biomedical engineering. 2021 Jun;5(6):498–508.

Published In

Nature biomedical engineering

DOI

EISSN

2157-846X

ISSN

2157-846X

Publication Date

June 2021

Volume

5

Issue

6

Start / End Page

498 / 508

Related Subject Headings

  • Stroke
  • Risk Factors
  • Risk Assessment
  • Retrospective Studies
  • Retinal Vessels
  • Retina
  • Photography
  • Myocardial Infarction
  • Middle Aged
  • Male