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
To quantify and compare peri-acetabular bone mineral density
(BMD) between a monoblock acetabular component using a metal-on-metal
(MoM) bearing and a modular titanium shell with a polyethylene (PE)
insert. The secondary outcome was to measure patient-reported clinical
function. A total of 50 patients (25 per group) were randomised to MoM
or metal-on-polyethlene (MoP). There were 27 women (11 MoM) and
23 men (14 MoM) with a mean age of 61.6 years (47.7 to 73.2). Measurements
of peri-prosthetic acetabular and contralateral hip (covariate)
BMD were performed at baseline and at one and two years’ follow-up.
The Western Ontario and McMaster Universities osteoarthritis index
(WOMAC), University of California, Los Angeles (UCLA) activity score,
Harris hip score, and RAND-36 were also completed at these intervals.Objectives
Methods
