John Charnley Award: Deep Learning Prediction of Hip Joint Center on Standard Pelvis Radiographs.

Journal: The Journal of arthroplasty
Published Date: Jul 1, 2022

Abstract

BACKGROUND: Accurate hip joint center (HJC) determination is critical for preoperative planning, intraoperative execution, clinical outcomes after total hip arthroplasty, and commonly used classification systems in primary and revision hip replacement. However, current methods of preoperative HJC estimation are prone to subjectivity and human error. The purpose of the study was to leverage deep learning (DL) to develop a rapid and objective HJC estimation tool on anteroposterior (AP) pelvis radiographs.

Authors

  • Seong Jun Jang
    Weill Cornell College of Medicine, New York, New York; Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York.
  • Kyle N Kunze
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
  • Jonathan M Vigdorchik
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York.
  • Seth A Jerabek
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York.
  • David J Mayman
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York.
  • Peter K Sculco
    The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA.

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