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
The aim of this study was to investigate the changes in femoral
trochlear morphology following surgical correction of recurrent
patellar dislocation associated with trochlear dysplasia in children. A total of 23 patients with a mean age of 9.6 years (7 to 11)
were included All had bilateral recurrent patellar dislocation associated
with femoral trochlear dysplasia. The knee with traumatic dislocation
at the time of presentation or that had dislocated most frequently
was treated with medial patellar retinacular plasty (Group S). The
contralateral knee served as a control and was treated conservatively
(Group C). All patients were treated between October 2008 and August
2013. The mean follow-up was 48.7 months (43 to 56). Axial CT scans
were undertaken in all patients to assess the trochlear morphological
characteristics on a particular axial image which was established
at the point with the greatest epicondylar width based on measurements
preoperatively and at the final follow-up.Aims
Patients and Methods
