Can images crowdsourced from the internet be used to train generalizable joint dislocation deep learning algorithms?

Journal: Skeletal radiology
PMID: 35624310

Abstract

OBJECTIVE: Deep learning has the potential to automatically triage orthopedic emergencies, such as joint dislocations. However, due to the rarity of these injuries, collecting large numbers of images to train algorithms may be infeasible for many centers. We evaluated if the Internet could be used as a source of images to train convolutional neural networks (CNNs) for joint dislocations that would generalize well to real-world clinical cases.

Authors

  • Jinchi Wei
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • David Li
    Department of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USA. david_li@college.harvard.edu.
  • David C Sing
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Jaewon Yang
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
  • Indeevar Beeram
    Department of Orthopaedic Surgery, Boston University School of Medicine, Boston, MA, USA.
  • Varun Puvanesarajah
  • Craig J Della Valle
    Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
  • Paul Tornetta
    Faculty of Medicine, University of Ottawa, Ontario, Canada.
  • Jan Fritz
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA. jfritz9@jhmi.edu.
  • Paul H Yi
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: Pyi10@jhmi.edu.

Keywords

External Resources

Popular Topics
Recent Journals