Abstract
AIMS: The aim of this study was to explore the relationship between the femoral head diameter (FHD) and the degree of subluxation in developmental dysplasia of the hip (DDH) patients, and develop a machine-learning model for predicting acetabular component size in total hip arthroplasty (THA) according to demographic data and FHD. METHODS: The FHD of 469 DDH patients from Longwood Valley medical database was measured, after excluding those with severe femoral head destruction, bone grafting, or augments. Its distribution and difference across Crowe and Hartofilakidis classifications were also assessed. Five machine-learning algorithms were developed to predict the size of the acetabular component, and the best model was determined according to the mean square error (MSE), root mean square error (RMSE), and R-squared values. The accuracy of the best model's cup size prediction was validated by comparing it with acetate templating and CT-based planning in a consecutive cohort from an independent institution. RESULTS: The FHD gradually decreased with increasing Crowe and Hartofilakidis classifications. The Pearson correlation coefficient between FHD and the size of the acetabular component was 0.60, indicating a moderate correlation. In the test set, the random forest model outperformed the other four models in terms of MSE (0.904), RMSE (0.951), and R-squared (0.919). In the external validation, the accuracy of this model was not significantly different from CT-based planning (80.0% vs 87.5%, p > 0.05), but outperformed acetate templating (80.0% vs 52.5%, p < 0.05), particularly for Crowe Type IV (81.8% vs 27.3%, p < 0.05). CONCLUSION: The FHD decreases with increasing degree of subluxation in DDH patients. The machine-learning model constructed by combining demographic parameters and FHD demonstrates significantly higher accuracy in acetabular component size planning compared to templating methods. This approach serving as an effective auxiliary tool or alternative when CT is unavailable.