The aims of this study were to develop an automatic system capable of calculating four radiological measurements used in the diagnosis and monitoring of cerebral palsy (CP)-related hip disease, and to demonstrate that these measurements are sufficiently accurate to be used in clinical practice. We developed a machine-learning system to automatically measure Reimer’s migration percentage (RMP), acetabular index (ACI), head shaft angle (HSA), and neck shaft angle (NSA). The system automatically locates points around the femoral head and acetabulum on pelvic radiographs, and uses these to calculate measurements. The system was evaluated on 1,650 pelvic radiographs of children with CP (682 females and 968 males, mean age 8.3 years (SD 4.5)). Each radiograph was manually measured by five clinical experts. Agreement between the manual clinical measurements and the automatic system was assessed by mean absolute deviation (MAD) from the mean manual measurement, type 1 and type 2 intraclass correlation coefficients (ICCs), and a linear mixed-effects model (LMM) for assessing bias.Aims
Methods
The aim of this study was to investigate the agreement in interpretation of the quality of the paediatric hip ultrasound examination, the reliability of geometric and morphological assessment, and the relationship between these measurements. Four investigators evaluated 60 hip ultrasounds and assessed their quality based the standard plane of Graf et al. They measured geometric parameters, described the morphology of the hip, and assigned the Graf grade of dysplasia. They analyzed one self-selected image and one randomly selected image from the ultrasound series, and repeated the process four weeks later. The intra- and interobserver agreement, and correlations between various parameters were analyzed.Aims
Methods
