Some patients presenting with hip pain and instability and underlying acetabular dysplasia (AD) do not experience resolution of symptoms after surgical management. Hip-spine syndrome is a possible underlying cause. We hypothesized that there is a higher frequency of radiological spine anomalies in patients with AD. We also assessed the relationship between radiological severity of AD and frequency of spine anomalies. In a retrospective analysis of registry data, 122 hips in 122 patients who presented with hip pain and and a final diagnosis of AD were studied. Two observers analyzed hip and spine variables using standard radiographs to assess AD. The frequency of lumbosacral transitional vertebra (LSTV), along with associated Castellvi grade, pars interarticularis defect, and spinal morphological measurements were recorded and correlated with radiological severity of AD.Aims
Methods
The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots.Aims
Methods
