This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.Aims
Methods
Aims. This study aimed to evaluate sagittal spinopelvic alignment (SSPA) in the early stage of
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
