THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution.

Journal: The Journal of arthroplasty
Published Date: Mar 1, 2024

Abstract

BACKGROUND: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants).

Authors

  • Pouria Rouzrokh
    Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN.
  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • John P Mickley
    Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), 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.

Keywords

External Resources

Popular Topics
Recent Journals