Development of a Deep Learning Model for Automating Implant Position in Total Hip Arthroplasty.

Journal: The Journal of arthroplasty
Published Date: Jan 27, 2025

Abstract

BACKGROUND: Novel methods for annotating antero-posterior pelvis radiographs and fluoroscopic images with deep-learning models have recently been developed. However, their clinical use has been limited. Therefore, the purpose of this study was to develop a deep learning model that could annotate clinically relevant pelvic landmarks on both radiographic and fluoroscopic images and automate total hip arthroplasty (THA)-relevant measurements.

Authors

  • Peter Y W Chan
    Department of Orthopaedic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.
  • Courtney E Baker
    Department of Orthopaedic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.
  • Yehyun Suh
    Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Daniel Moyer
    Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States.
  • J Ryan Martin
    Department of Orthopaedic Surgery, Vanderbilt University Medical Center, 1215 21st Avenue South, Nashville, TN, 37232, USA; Vanderbilt Institute for Surgery and Engineering, 1161 21st Ave South, Nashville, TN, 37212, USA. Electronic address: john.martin@vumc.org.

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