Excellent outcomes have been reported following CT-based robotic arm-assisted total hip arthroplasty (rTHA) compared with manual THA; however, its superiority over CT-based navigation THA (nTHA) remains unclear. This study aimed to determine whether a CT-based robotic arm-assisted system helps surgeons perform accurate cup placement, minimizes leg length, and offsets discrepancies more than a CT-based navigation system. We studied 60 hips from 54 patients who underwent rTHA between April 2021 and August 2023, and 45 hips from 44 patients who underwent nTHA between January 2020 and March 2021 with the same target cup orientation at the Department of Orthopedic Surgery at Ozu Memorial Hospital, Japan. After propensity score matching, each group had 37 hips. Postoperative acetabular component position and orientation were measured using the planning module of the CT-based navigation system. Postoperative leg length and offset discrepancies were evaluated using postoperative CT in patients who have unilateral hip osteoarthritis.Aims
Methods
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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