Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

Journal: The Journal of arthroplasty
PMID: 38007206

Abstract

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF.

Authors

  • Seong J Jang
    Department of Orthopaedic Surgery, Adult Reconstruction and Joint Replacement Service, New York, New York, USA.
  • Kyle Alpaugh
    Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, Massachusetts.
  • Kyle N Kunze
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
  • Tim Y Li
    Weill Cornell College of Medicine, 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.
  • 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.
  • Elizabeth B Gausden
    Department of Orthopedic Surgery, 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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