Detecting upper extremity native joint dislocations using deep learning: A multicenter study.

Journal: Clinical imaging
PMID: 36183620

Abstract

OBJECTIVE: Joint dislocations are orthopedic emergencies that require prompt intervention. Automatic identification of these injuries could help improve timely patient care because diagnostic delays increase the difficulty of reduction. In this study, we developed convolutional neural networks (CNNs) to detect elbow and shoulder dislocations, and tested their generalizability on external datasets.

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.
  • Indeevar Beeram
    Department of Orthopaedic Surgery, Boston University School of Medicine, Boston, MA, USA.
  • Varun Puvanesarajah
  • 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.

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