A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

Journal: The Journal of arthroplasty
PMID: 37236288

Abstract

BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single deep learning model to annotate certain anatomical structures and landmarks on antero-posterior (AP) pelvis radiographs.

Authors

  • Kellen L Mulford
    Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Laboratory, Mayo Clinic, Rochester, Minnesota.
  • Quinn J Johnson
    Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, Minnesota.
  • Tala Mujahed
    Mayo Clinic Alix School of Medicine, Scottsdale, Arizona.
  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Pouria Rouzrokh
    Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN.
  • John P Mickley
    Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, Minnesota.
  • Michael J Taunton
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN; Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Cody C Wyles
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.

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