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
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To evaluate the effect of ultrasound-targeted simvastatin-loaded microbubble destruction (UTMD In vitro, OA chondrocytes were treated with ultrasound (US), US-targeted microbubble destruction (UTMD), simvastatin (SV), and UTMDAims
Methods
The primary purpose of this meta-analysis was to determine whether statin usage could reduce the risk of glucocorticoid-related osteonecrosis in animal models. A systematic literature search up to May 2015 was carried out using the PubMed, Ovid, EBM reviews, ISI Web of Science, EBSCO, CBM, CNKI databases with the term and boolean operators: statins and osteonecrosis in all fields. Risk ratio (RR), as the risk estimate of specific outcome, was calculated along with 95% confidence intervals (CI). The methodological quality of individual studies was assessed using a quantitative tool based on the updated Stroke Therapy Academic Industry Roundtable (STAIR) recommendations.Objectives
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