Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images-Reference-Cited by-同舟云学术

Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images

Published:2021-06 Issue:6 Volume:5 Page:533-545
ISSN:2157-846X
Container-title:Nature Biomedical Engineering
language:en
Short-container-title:Nat Biomed Eng

Author:

Zhang Kang^ORCID,Liu Xiaohong,Xu Jie,Yuan Jin,Cai Wenjia,Chen Ting^ORCID,Wang Kai,Gao Yuanxu^ORCID,Nie Sheng,Xu Xiaodong^ORCID,Qin Xiaoqi,Su Yuandong,Xu Wenqin,Olvera Andrea,Xue Kanmin,Li Zhihuan,Zhang Meixia,Zeng Xiaoxi,Zhang Charlotte L.,Li Oulan,Zhang Edward E.,Zhu Jie,Xu Yiming,Kermany Daniel,Zhou Kaixin,Pan Ying,Li Shaoyun,Lai Iat Fan,Chi Ying,Wang Changuang,Pei Michelle,Zang Guangxi,Zhang Qi,Lau Johnson,Lam Dennis,Zou Xiaoguang,Wumaier Aizezi,Wang Jianquan,Shen Yin,Hou Fan Fan,Zhang Ping,Xu Tao,Zhou Yong^ORCID,Wang Guangyu^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,Biotechnology

Link

http://www.nature.com/articles/s41551-021-00745-6.pdf

Reference43 articles.

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2. Levin, A. et al. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. Lancet 390, 1888–1917 (2017).

3. Kooman, J. P., Kotanko, P., Schols, A. M., Shiels, P. G. & Stenvinkel, P. Chronic kidney disease and premature ageing. Nat. Rev. Nephrol. 10, 732–742 (2014).

4. Saeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res. Clin. Pract. 157, 107843 (2019).

5. Wong, T. Y. & Sabanayagam, C. The war on diabetic retinopathy: where are we now. Asia Pac. J. Ophthalmol. 8, 448–456 (2019).

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