Femoral component anteversion is an important factor in the success of total hip arthroplasty (THA). This retrospective study aimed to investigate the accuracy of femoral component anteversion with the Mako THA system and software using the Exeter cemented femoral component, compared to the Accolade II cementless femoral component. We reviewed the data of 30 hips from 24 patients who underwent THA using the posterior approach with Exeter femoral components, and 30 hips from 24 patients with Accolade II components. Both groups did not differ significantly in age, sex, BMI, bone quality, or disease. Two weeks postoperatively, CT images were obtained to measure acetabular and femoral component anteversion.Aims
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This study aimed to investigate the incidence of ≥ 5 mm asymmetry in lower and whole leg lengths (LLs) in patients with unilateral osteoarthritis (OA) secondary to developmental dysplasia of the hip (DDH-OA) and primary hip osteoarthritis (PHOA), and the relationship between lower and whole LL asymmetries and femoral length asymmetry. In total, 116 patients who underwent unilateral total hip arthroplasty were included in this study. Of these, 93 had DDH-OA and 23 had PHOA. Patients with DDH-OA were categorized into three groups: Crowe grade I, II/III, and IV. Anatomical femoral length, femoral length greater trochanter (GT), femoral length lesser trochanter (LT), tibial length, foot height, lower LL, and whole LL were evaluated using preoperative CT data of the whole leg in the supine position. Asymmetry was evaluated in the Crowe I, II/III, IV, and PHOA groups.Aims
Methods
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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