Auxiliary diagnosis of developmental dysplasia of the hip by automated detection of Sharp's angle on standardized anteroposterior pelvic radiographs

Developmental dysplasia of the hip (DDH) is common, and features a widened Sharp's angle as observed on pelvic x-ray images. Determination of Sharp's angle, essential for clinical decisions, can overwhelm the workload of orthopedic surgeons. To aid diagnosis of DDH and reduce false negative diagnoses, a simple and cost-effective tool is proposed. The model was designed using artificial intelligence (AI), and evaluated for its ability to screen anteroposterior pelvic radiographs automatically, accurately, and efficiently. Orthotopic anterior pelvic x-ray images were retrospectively collected (n = 11574) from the PACS (Picture Archiving and Communication System) database at Second Hospital of Jilin University. The Mask regional convolutional neural network (R-CNN) model was utilized and finely modified to detect 4 key points that delineate Sharp's angle. Of these images, 11,473 were randomly selected, labeled, and used to train and validate the modified Mask R-CNN model. A test dataset comprised the remaining 101 images. Python-based utility software was applied to draw and calculate Sharp's angle automatically. The diagnoses of DDH obtained via the model or the traditional manual drawings of 3 orthopedic surgeons were compared, each based on the degree of Sharp's angle, and these were then evaluated relative to the final clinical diagnoses (based on medical history, symptoms, signs, x-ray films, and computed tomography images). Sharp's angles on the left and right measured via the AI model (40.07° ± 4.09° and 40.65° ± 4.21°), were statistically similar to that of the surgeons’ (39.35° ± 6.74° and 39.82° ± 6.99°). The measurement time required by the AI model (1.11 ± 0.00 s) was significantly less than that of the doctors (86.72 ± 1.10, 93.26 ± 1.12, and 87.34 ± 0.80 s). The diagnostic sensitivity, specificity, and accuracy of the AI method for diagnosis of DDH were similar to that of the orthopedic surgeons; the diagnoses of both were moderately consistent with the final clinical diagnosis. The proposed AI model can automatically measure Sharp's angle with a performance similar to that of orthopedic surgeons, but requires far less time. The AI model may be a viable auxiliary to clinical diagnosis of DDH.