MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.

Journal: European radiology
PMID:

Abstract

OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries.

Authors

  • Ming Ni
    Department of Orthopaedics, Chinese People's Liberation Army General Hospital (301 Hospital), 28 Fuxing Rd, 100853, Beijing, China.
  • Yuqing Zhao
    Faculty of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, Yunnan 650201, PR China. Electronic address: kmyuqing@163.com.
  • Lihua Zhang
    Department of Mathematics, University of California, Irvine, CA, 92697, USA.
  • Wen Chen
    School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan, China.
  • Qizheng Wang
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
  • Chunyan Tian
    Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China. huishuy@bjmu.edu.cn.
  • Huishu Yuan
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.