Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph
Issued Date
2025-12-01
Resource Type
eISSN
20770383
Scopus ID
2-s2.0-105026114226
Journal Title
Journal of Clinical Medicine
Volume
14
Issue
24
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Clinical Medicine Vol.14 No.24 (2025)
Suggested Citation
Wongsak S., Janyawongchot T., Sri-Utenchai N., Owasirikul D., Jaovisidha S., Woratanarat P., Sa-Ngasoongsong P. Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph. Journal of Clinical Medicine Vol.14 No.24 (2025). doi:10.3390/jcm14248689 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113775
Title
Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Preoperative templating is an essential step in hip arthroplasty (HA), guiding implant selection and reducing surgical complications. It is typically performed using acetate templates or digital software. These methods, however, depend on the surgeon’s experience and may be limited by cost and availability. This study aimed to develop and validate a deep learning (DL) model using plain radiographs to predict implant sizes in HA. Methods: This retrospective study included patients who underwent primary HA using a cementless CORAIL<sup>®</sup> femoral stem and PINNACLE<sup>®</sup> acetabular cup. The DL model was trained on 688 preoperative anteroposterior (AP) hip radiographs and validated temporally on 98 additional cases. Implant sizes predicted by the DL model were compared with on-screen templating (acetate templates overlaid on digital images). The actual implanted size was used as the reference standard. Accuracy, mean absolute error (MAE), and root mean square error (RMSE) were calculated. Logistic regression was performed to identify factors influencing prediction accuracy. Results: The DL model showed higher accuracy than the on-screen templating for the acetabular cup (88.9% [77.4% to 95.8%] vs. 83.3% [70.7% to 90.2%]) and femoral stem components (85.7% [77.2% to 92.0%] vs. 81.6% [72.5% to 88.7%]), while the on-screen method performed better for the bipolar head (93.2% [81.3% to 98.6%] vs. 72.7% [57.2% to 85.0%]). MAE and RMSE were comparable between the methods for acetabular and femoral stem components (all p > 0.05), with statistically significant differences observed only in the bipolar head (p < 0.01 and 0.02, respectively). Although logistic regression analysis showed trends toward higher accuracy in acetabular size prediction among women and those with shorter height, no demographic factors were statistically significant predictors of accuracy. Conclusions: A DL model using only plain radiographs can accurately predict implant sizes in HA, particularly for the acetabulum and femoral stem. These findings suggest that the DL-based model could be a useful tool in preoperative planning. With further refinement to improve generalizability, this approach could be useful in a routine clinical setting in the future.