Résumé
Developmental dysplasia of the hip (DDH) is a common skeletal malformation in children and the prominent cause of hip osteoarthritis and lower limb disability. The therapeutic difficulty and effect of DDH are closely related to an early and proper diagnosis. Hip ultrasonography and anteroposterior pelvic radiography are preferred depending on the presence of the secondary ossification center of the femoral head. Conventional diagnostic methods primarily relied on manual measurements and empirical judgments by clinicians, which were laborious and generally lacked reliability. The effective integration of medical imaging and artificial intelligence algorithms is expected to improve the diagnosis of pediatric DDH and enhance the efficiency of clinical diagnosis and treatment. Segmentation algorithms based on the extraction of local geometric features, 3D map search-based segmentation algorithms, and deep learning networks were utilized to assist in analyzing hip ultrasound images, calculating key dysplasia indicators, and diagnosing DDH in infants under 4-6 months. Computer-aided techniques, such as bone edge detection and template matching algorithms, deep transfer learning algorithms, and local-global feature mining convolutional neural networks were used to automatically identify bony landmarks on pelvic radiographs for measuring hip parameters and evaluating DDH in children over 4-6 months. However, there were several crucial problems in the clinical application of the artificial intelligence model for the auxiliary diagnosis of DDH due to technical limitations and insufficient understanding of researchers. This paper aims to review the progress of application in the medical artificial intelligence technology for the clinical auxiliary diagnosis of DDH. The author also provides references for future research for truly intelligent diagnostic tools.