Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.

Journal: European radiology
Published Date:

Abstract

OBJECTIVE: To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.

Authors

  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Yang Huang
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
  • Bo-Wen Zhuang
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
  • Guang-Jian Liu
    Department of Medical Ultrasonics, The Sixth Affiliated Hospital of Sun Yat-sen University (Guangdong Gastrointestinal Hospital), Guangzhou, China.
  • Hang-Tong Hu
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Jin-Yu Liang
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
  • Zhu Wang
    National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
  • Xiao-Wen Huang
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
  • Chu-Qing Zhang
    Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
  • Si-Min Ruan
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
  • Xiao-Yan Xie
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Ming Kuang
    School of Medicine, Jiangsu University, Zhenjiang 212013, China.
  • Ming-De Lu
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
  • Li-Da Chen
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China. chenlda@mail.sysu.edu.cn.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.