Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study.

Journal: Journal of X-ray science and technology
PMID:

Abstract

BACKGROUND: Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming.

Authors

  • Lin Guo
    Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
  • Li Xia
    Department of Anesthesiology, Gansu Provincial Hospital, Lanzhou, China.
  • Qiuting Zheng
    Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
  • Bin Zheng
    School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.
  • Stefan Jaeger
    Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States.
  • Maryellen L Giger
    Department of Radiology, University of Chicago, 5841 S Maryland Ave., Chicago, IL, 60637, USA.
  • Jordan Fuhrman
    Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, 60637, USA. jdfuhrman@uchicago.edu.
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Fleming Y M Lure
    Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
  • Hongjun Li
    School of Agricultural Engineering and Food Science, Shandong University of Technology, Zhangdian District, No. 12, Zhangzhou Road, Zibo, Shandong Province, China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.