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
The main aims were to identify risk factors predictive of a radiolucent line (RLL) around the acetabular component with an interface bioactive bone cement (IBBC) technique in the first year after THA, and evaluate whether these risk factors influence the development of RLLs at five and ten years after THA. A retrospective review was undertaken of 980 primary cemented THAs in 876 patients using cemented acetabular components with the IBBC technique. The outcome variable was any RLLs that could be observed around the acetabular component at the first year after THA. Univariate analyses with univariate logistic regression and multivariate analyses with exact logistic regression were performed to identify risk factors for any RLLs based on radiological classification of hip osteoarthritis.Aims
Methods
