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
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Alarm over the reported high failure rates for metal-on-metal (MoM) hip implants as well as their potential for locally aggressive Adverse Reactions to Metal Debris (ARMDs) has prompted government agencies, internationally, to recommend the monitoring of patients with MoM hip implants. Some have advised that a blood ion level >7 µg/L indicates potential for ARMDs. We report a systematic review and meta-analysis of the performance of metal ion testing for ARMDs. We searched MEDLINE and EMBASE to identify articles from which it was possible to reconstruct a 2 × 2 table. Two readers independently reviewed all articles and extracted data using explicit criteria. We computed a summary receiver operating curve using a Bayesian random-effects hierarchical model.Objectives
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