Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment. A consecutive preoperative sample of 130 HD patients (256 hips) was used. This cohort included 82.3% females (n = 107) and 17.7% males (n = 23) with median patient age of 28.6 years (interquartile range (IQR) 22.5 to 37.2). Three trained readers’ measurements were compared to AI outputs of lateral centre-edge angle (LCEA), caput-collum-diaphyseal (CCD) angle, pelvic obliquity, Tönnis angle, Sharp’s angle, and femoral head coverage. Intraclass correlation coefficients (ICC) and Bland-Altman analyses were obtained.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
Methods
Sagittal lumbar pelvic alignment alters with posterior pelvic tilt (PT) following total hip arthroplasty (THA) for developmental dysplasia of the hip (DDH). The individual value of pelvic sagittal inclination (PSI) following rebalancing of lumbar-pelvic alignment is unknown. In different populations, PT regresses in a linear relationship with pelvic incidence (PI). PSI and PT have a direct relationship to each other via a fixed individual angle ∠γ. This study aimed to investigate whether the new PI created by acetabular component positioning during THA also has a linear regression relationship with PT/PSI when lumbar-pelvic alignment rebalances postoperatively in patients with Crowe type III/IV DDH. Using SPINEPARA software, we measured the pelvic sagittal parameters including PI, PT, and PSI in 61 patients with Crowe III/IV DDH. Both PSI and PT represent the pelvic tilt state, and the difference between their values is ∠γ (PT = PSI + ∠γ). The regression equation between PI and PT at one year after THA was established. By substituting ∠γ, the relationship between PI and PSI was also established. The Bland-Altman method was used to evaluate the consistency between the PSI calculated by the linear regression equation (ePSI) and the actual PSI (aPSI) measured one year postoperatively.Aims
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