THA-AID: Deep Learning Tool for Total Hip Arthroplasty Automatic Implant Detection With Uncertainty and Outlier Quantification.

Journal: The Journal of arthroplasty
PMID: 37770007

Abstract

BACKGROUND: Revision total hip arthroplasty (THA) requires preoperatively identifying in situ implants, a time-consuming and sometimes unachievable task. Although deep learning (DL) tools have been attempted to automate this process, existing approaches are limited by classifying few femoral and zero acetabular components, only classify on anterior-posterior (AP) radiographs, and do not report prediction uncertainty or flag outlier data.

Authors

  • 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.
  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Shahriar Faghani
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Mana Moassefi
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • William R Schulz
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota.
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • 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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