This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.Aims
Methods
In total hip arthroplasty (THA), the cementless, tapered-wedge stem design contributes to achieving initial stability and providing optimal load transfer in the proximal femur. However, loading conditions on the femur following THA are also influenced by femoral structure. Therefore, we determined the effects of tapered-wedge stems on the load distribution of the femur using subject-specific finite element models of femurs with various canal shapes. We studied 20 femurs, including seven champagne flute-type femurs, five stovepipe-type femurs, and eight intermediate-type femurs, in patients who had undergone cementless THA using the Accolade TMZF stem at our institution. Subject–specific finite element (FE) models of pre- and post-operative femurs with stems were constructed and used to perform FE analyses (FEAs) to simulate single-leg stance. FEA predictions were compared with changes in bone mineral density (BMD) measured for each patient during the first post-operative year.Objectives
Patients and Methods
