Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph.

Area of Science:

• Orthopedic Surgery
• Artificial Intelligence in Medicine
• Medical Imaging Analysis

Background:

• Preoperative templating is crucial for hip arthroplasty (HA) implant selection and complication reduction.
• Current templating methods (acetate, digital software) rely on surgeon experience and have limitations.
• Developing automated, accurate templating tools is essential for optimizing HA outcomes.

Purpose of the Study:

• To develop and validate a deep learning (DL) model for predicting hip arthroplasty implant sizes using plain radiographs.
• To compare the accuracy of the DL model against traditional on-screen templating methods.

Main Methods:

• Retrospective study of 786 primary hip arthroplasty cases using cementless CORAIL femoral stems and PINNACLE acetabular cups.
• A DL model was trained on 688 preoperative anteroposterior hip radiographs and validated on 98 cases.
• DL model predictions were compared to on-screen templating and actual implanted sizes.

Main Results:

• The DL model demonstrated higher accuracy for acetabular cup (88.9%) and femoral stem (85.7%) size prediction compared to on-screen templating.
• On-screen templating was more accurate for bipolar head prediction (93.2%) than the DL model (72.7%).
• Mean absolute error and root mean square error were comparable for acetabular and femoral components between methods.

Conclusions:

• A DL model utilizing plain radiographs can accurately predict hip arthroplasty implant sizes, especially for acetabular and femoral components.
• The DL-based approach shows potential as a valuable tool for preoperative planning in hip arthroplasty.
• Further refinement for generalizability could enable routine clinical application of this AI-driven templating method.