Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs.

Journal: The Journal of arthroplasty
PMID: 33663890

Abstract

BACKGROUND: Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study is to illustrate the potential of a convolutional neural network model to assess the risk of hip dislocation based on postoperative anteroposterior pelvis radiographs.

Authors

  • Pouria Rouzrokh
    Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN.
  • Taghi Ramazanian
    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.
  • Kenneth A Philbrick
    1 Department of Radiology, Radiology Informatics Laboratory, Mayo Clinic, 3507 17th Ave NW, Rochester, MN 55901.
  • Jason C Cai
    Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN.
  • Michael J Taunton
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN; Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Hilal Maradit Kremers
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN; Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
  • David G Lewallen
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.

Keywords

External Resources

Popular Topics
Recent Journals