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

Pouria Rouzrokh1, Bardia Khosravi1, John P Mickley2

  • 1Department of Radiology, Mayo Clinic, Minnesota.

The Journal of Arthroplasty
|August 24, 2023
PubMed
Summary

This study presents THA-Net, an AI tool that creates realistic postoperative hip replacement X-rays from single preoperative images. The generated images show improved surgical execution compared to real ones.

Keywords:
artificial intelligencedeep learningdenoising diffusion probabilistic modelsmachine learningtemplatingtotal hip arthroplasty

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Orthopedic Surgery

Background:

  • Deep learning algorithms are advancing medical imaging analysis.
  • Simulating postoperative outcomes aids surgical planning and education.
  • Total hip arthroplasty (THA) requires precise pre-operative planning.

Purpose of the Study:

  • Introduce THA-Net, a deep learning inpainting algorithm.
  • Simulate postoperative total hip arthroplasty (THA) radiographs from single preoperative images.
  • Enable unconditional (algorithm-chosen) or conditional (surgeon-chosen) implant predictions.

Main Methods:

  • THA-Net processes preoperative radiographs to generate synthetic postoperative images with THA implants.
  • The algorithm was trained on 356,305 radiograph pairs from 14,357 patients.
  • Outputs were evaluated for surgical validity and realism using human and software-based criteria.

Main Results:

  • Synthetic radiographs demonstrated significantly higher surgical validity than real ones.
  • Blinded expert reviewers could not differentiate the realism of synthetic versus real radiographs.
  • Deep learning models confirmed the excellent validity and realism of synthetic images.

Conclusions:

  • THA-Net serves as a next-generation templating tool for total hip arthroplasty.
  • Synthetic radiographs generated by THA-Net exceed the surgical execution quality of real training data.
  • The tool has potential for patient-specific planning, robotics, navigation, and augmented reality applications.