Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD.

Authors

  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Changxuan Sun
    Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China.
  • Yuwei Shi
    Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China.
  • Julianna C Hsing
    Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine, Stanford, CA, USA.
  • Aya Kamya
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Cody Auston Keller
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Neha Antil
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Daniel Rubin
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Hongxia Wang
    Department of Mathematics, National University of Defense Technology, Changsha, China. Electronic address: wanghongxia@nudt.edu.cn.
  • Haochao Ying
  • Xueyin Zhao
    Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China.
  • Yi-Hsuan Wu
    Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, No.1, Changde St., Zhongzheng Dist, Taipei City, 100, Taiwan.
  • Mindie Nguyen
    Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
  • Ying Lu
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
  • Fei Yang
    Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, University of South China, Hengyang 421001, China.
  • Pinton Huang
    Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Ann W Hsing
    Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 780 Welch Road, CJ Huang Building, Suite 250D, Stanford, CA, 94305, USA. annhsing@stanford.edu.
  • Jian Wu
    Department of Medical Technology, Jiangxi Medical College, Shangrao, Jiangxi, China.
  • Shankuan Zhu
    Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China. zsk@zju.edu.cn.