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Artificial Intelligence-Based Surgery Support Model Using Intraoperative Radiographs for Assessing the Acetabular

Yoshitomo Saiki1, Tamon Kabata2, Yoshitomo Kajino2

  • 1Faculty of Health Science, Department of Rehabilitation Physical Therapy, Fukui Health Science University, Fukui City, Fukui, Japan; Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kanazawa University, Kanazawa City, Ishikawa, Japan.

The Journal of Arthroplasty
|September 14, 2024
PubMed
Summary

An AI model accurately estimates acetabular component angles during total hip arthroplasty using intraoperative radiographs. This artificial intelligence tool shows excellent accuracy, potentially reducing adverse postoperative events.

Keywords:
acetabular component anglearthroplastyartificial intelligencehipradiographsurgery support

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Area of Science:

  • Orthopedic Surgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate acetabular component positioning is crucial in total hip arthroplasty (THA).
  • Intraoperative assessment of acetabular component angles can be challenging.
  • Existing methods may lack precision or real-time feedback.

Purpose of the Study:

  • To develop and validate an AI-based surgical support model.
  • To assess the acetabular component angle using intraoperative radiographs.
  • To verify the accuracy of the AI model in THA.

Main Methods:

  • Developed an AI model using 268 preoperative and intraoperative pelvic radiographs (amplified to 536).
  • Used a computed tomography-based navigation system for ground truth anteversion and inclination angles.
  • Annotated bone landmarks on radiographs as predictor variables.
  • Evaluated model accuracy using Mean Absolute Error (MAE) and R² on internal and external test sets.

Main Results:

  • Internal test set: MAE of 2.19 and R² of 0.850 for anteversion; MAE of 1.18 and R² of 0.805 for inclination.
  • External test set: MAE of 2.78 and R² of 0.789 for anteversion; MAE of 1.56 and R² of 0.744 for inclination.
  • Demonstrated excellent estimation accuracy on the external test set.

Conclusions:

  • Successfully developed an AI model for accurate acetabular component angle assessment.
  • The model demonstrated high accuracy in estimating angles from intraoperative radiographs.
  • This AI tool has the potential to improve THA outcomes and reduce postoperative complications.