Artificial Intelligence Approach in Hip Prosthesis Identification and Addressing Radiographic Outcome Measures
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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Arthroplasty Today |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352344125001049 |
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| Summary: | 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. |
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| ISSN: | 2352-3441 |