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Original research
A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography

Authors

  • Xusheng Jiang Department of Cardiac Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China PubMed articlesGoogle scholar articles
  • Jin Yu Department of Ultrasound Diagnosis, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China PubMed articlesGoogle scholar articles
  • Jingjing Ye Department of Ultrasound Diagnosis, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China PubMed articlesGoogle scholar articles
  • Weijie Jia Innovation Center for Child Health, Binjiang Institute of Zhejiang University, Hangzhou, China PubMed articlesGoogle scholar articles
  • Weize Xu Department of Cardiac Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China PubMed articlesGoogle scholar articles
  • Qiang Shu Department of Cardiac Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China PubMed articlesGoogle scholar articles
  1. Correspondence to Prof. Qiang Shu; shuqiang{at}zju.edu.cn
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Citation

Jiang X, Yu J, Ye J, et al
A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography
World Journal of Pediatric Surgery 2023;6:e000580. doi: 10.1136/wjps-2023-000580

Publication history

  • Received February 3, 2023
  • Accepted March 26, 2023
  • First published June 5, 2023.
Online issue publication 
July 19, 2023

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© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.