An automatic framework for perioperative risks classification from retinal images of complex congenital heart disease patients-Reference-Cited by-同舟云学术

An automatic framework for perioperative risks classification from retinal images of complex congenital heart disease patients

Published:2021-09-01 Issue:2 Volume:13 Page:471-483
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Ng Wing W. Y.^ORCID,Liang Haicong,Peng Qingsheng,Zhong Cankun,Dong Xinran,Huang Zhongning,Zhong Pingting,Li Cong,Xu Minghui,Sun Yunxia,Yu Honghua,Yang Xiaohong

Funder

National Natural Science Foundation of China

Guangdong Province Science and Technology Plan Project

Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou

Medical Scientific Research Foundation of Guangdong Province of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-021-01419-0.pdf

Reference49 articles.

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2. Li YJ, Yang YQ (2017) An update on the molecular diagnosis of congenital heart disease: focus on loss-of-function mutations. Expert Rev Mol Diagn 17(4):1–9

3. Canobbio MM, Warnes CA, Aboulhosn J, Connolly HM, Khanna A, Koos BJ, Mital S, Rose C, Silversides C, Stout K (2017) Management of pregnancy in patients with complex congenital heart disease: a scientific statement for healthcare professionals from the American Heart Association. Circulation 135(8):50–87

4. Verheugt CL, Uiterwaal CS, Grobbee DE, Mulder BJ (2008) Long-term prognosis of congenital heart defects: a systematic review. Int J Cardiol 131:25–32

5. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR (2018) Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2:158–164

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