Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study.

Journal: The Lancet. Digital health
PMID:

Abstract

BACKGROUND: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed to engineer deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.

Authors

  • Wei Xiao
    Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhenjiang Province, China.
  • Xi Huang
    Institute of Computing Technology(ICT), Chinese Academy of Sciences(CAS), Beijing, China.
  • Jing Hui Wang
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Duo Ru Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Yi Zhu
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Chuan Chen
    Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Ya Han Yang
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Jun Xiao
    Zhejiang University, 38 Zheda Road, Hangzhou 310058, Zhejiang, China.
  • Lan Qin Zhao
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Ji-Peng Olivia Li
    Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.
  • Carol Yim-Lui Cheung
    Singapore Eye Research Institute, Singapore National Eye Center, Singapore.
  • Yoshihiro Mise
    Department of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
  • Zhi Yong Guo
    Organ Transplant Centre, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Yun Feng Du
    Vistel AI Lab, Visionary Intelligence, Beijing, China.
  • Bai Bing Chen
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Jing Xiong Hu
    Department of Hepatobiliary Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Xiao Shan Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Wen Wen
    School of Computer Science, Guangdong University of Technology, China. Electronic address: wwen@gdut.edu.cn.
  • Yi Zhi Liu
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Wei Rong Chen
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Yue Si Zhong
    Department of Hepatobiliary Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: zhyues@mail.sysu.edu.cn.
  • Hao Tian Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China; Centre for Precision Medicine, Sun Yat-sen University, Guangzhou, China. Electronic address: linht5@mail.sysu.edu.cn.