Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD.

Authors

  • Jiaxing Sun
    Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China.
  • Ximing Liao
    Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China.
  • Yusheng Yan
    College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Jian Sun
    Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, Colorado, 80210, United States of America.
  • Weixiong Tan
    Beijing Infervision Technology Co. Ltd., Beijing, 100025, China.
  • Baiyun Liu
    Infervision, Beijing, China.
  • Jiangfen Wu
    Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao St., Qinhuai District, Nanjing, 210016, Jiangsu Province, China, wjfyunzhu@163.com.
  • Qian Guo
    State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China.
  • Shaoyong Gao
    Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China.
  • Zhang Li
    College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Qiang Li
    Department of Dermatology, Air Force Medical Center, PLA, Beijing, People's Republic of China.