Artificial intelligence approach in hip prosthesis identification and addressing radiographic outcome measures
Author(s)
Type
Journal Article
Abstract
Background
Radiographic assessment is crucial for the success of a hip arthroplasty procedure as a correctly positioned prosthesis indicates favorable long-term outcomes. This project aims to develop a novel artificial intelligence (AI)–based method that can (1) automatically identify the presence of a hip resurfacing prosthesis in radiographs and (2) calculate the radiographic neck-shaft angle (NSA) of the prosthesis from 2-dimensional plane images using both anterior-posterior (AP) and lateral radiographs with high accuracy.
Methods
Using a computer vision and pattern recognition algorithm, the femur shaft and prosthesis regions were identified, and their respective angles were extracted for NSA calculation. A neural network (NN) was then trained using clinician-generated AP radiograph NSAs as ground truths and AI-generated AP and lateral NSAs as features. Spearman's correlation and Kruskal-Wallis tests were calculated to explore any significant association between the final AI-generated and clinician-generated AP radiographic NSAs. Mean absolute error (MAE) and R-squared values were calculated with and without the NN model to identify the model's accuracy and variability.
Results
There was a statistically significant correlation between the final AI-generated AP radiographic NSAs and the clinician-generated AP radiographic NSAs (rs = 0.93, P < .01). MAE, R2, and rs without the NN were 3.09, 0.37, and 0.83 (P < .01), respectively. MAE and R2 with the NN were 1.94 and 0.53, respectively.
Conclusions
This study demonstrates that the identification of hip resurfacing prostheses using AI is feasible. By incorporating additional features such as the lateral NSA, the model can provide an accurate prediction of the AP radiographic NSA, closely approximating the ground truth.
Radiographic assessment is crucial for the success of a hip arthroplasty procedure as a correctly positioned prosthesis indicates favorable long-term outcomes. This project aims to develop a novel artificial intelligence (AI)–based method that can (1) automatically identify the presence of a hip resurfacing prosthesis in radiographs and (2) calculate the radiographic neck-shaft angle (NSA) of the prosthesis from 2-dimensional plane images using both anterior-posterior (AP) and lateral radiographs with high accuracy.
Methods
Using a computer vision and pattern recognition algorithm, the femur shaft and prosthesis regions were identified, and their respective angles were extracted for NSA calculation. A neural network (NN) was then trained using clinician-generated AP radiograph NSAs as ground truths and AI-generated AP and lateral NSAs as features. Spearman's correlation and Kruskal-Wallis tests were calculated to explore any significant association between the final AI-generated and clinician-generated AP radiographic NSAs. Mean absolute error (MAE) and R-squared values were calculated with and without the NN model to identify the model's accuracy and variability.
Results
There was a statistically significant correlation between the final AI-generated AP radiographic NSAs and the clinician-generated AP radiographic NSAs (rs = 0.93, P < .01). MAE, R2, and rs without the NN were 3.09, 0.37, and 0.83 (P < .01), respectively. MAE and R2 with the NN were 1.94 and 0.53, respectively.
Conclusions
This study demonstrates that the identification of hip resurfacing prostheses using AI is feasible. By incorporating additional features such as the lateral NSA, the model can provide an accurate prediction of the AP radiographic NSA, closely approximating the ground truth.
Date Issued
2025-06-01
Date Acceptance
2025-04-23
Citation
Arthroplasty Today, 2025, 33
ISSN
2352-3441
Publisher
Elsevier
Journal / Book Title
Arthroplasty Today
Volume
33
Copyright Statement
© 2025 The Authors. Published by Elsevier Inc. on behalf of The American Association of Hip and Knee Surgeons. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Publication Status
Published
Article Number
101717
Date Publish Online
2025-06-03