Detecting total hip arthroplasty dislocations using deep learning: clinical and Internet validation.

Journal: Emergency radiology
PMID: 35608786

Abstract

OBJECTIVE: Periprosthetic dislocations of total hip arthroplasty (THA) are time-sensitive injuries, as the longer diagnosis and treatment are delayed, the more difficult they are to reduce. Automated triage of radiographs with dislocations could help reduce these delays. We trained convolutional neural networks (CNNs) for the detection of THA dislocations, and evaluated their generalizability by evaluating them 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.
  • 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.

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