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
Parathyroid hormone (PTH) (1-34) exhibits potential in preventing degeneration in both cartilage and subchondral bone in osteoarthritis (OA) development. We assessed the effects of PTH (1-34) at different concentrations on bone and cartilage metabolism in a collagenase-induced mouse model of OA and examined whether PTH (1-34) affects the JAK2/STAT3 signalling pathway in this process. Collagenase-induced OA was established in C57Bl/6 mice. Therapy with PTH (1-34) (10 μg/kg/day or 40 μg/kg/day) was initiated immediately after surgery and continued for six weeks. Cartilage pathology was evaluated by gross visual, histology, and immunohistochemical assessments. Cell apoptosis was analyzed by TUNEL staining. Microcomputed tomography (micro-CT) was used to evaluate the bone mass and the microarchitecture in subchondral bone.Aims
Methods
