Model construction and application for automated measurement of CE angle on pelvis orthograph based on MASK-R-CNN algorithm

Abstract Developmental dysplasia of the hip (DDH) is a common orthopedic disease. A simple and cost-effective scientific tool for assisting the early diagnosis of DDH is urgently needed. This study proposed a new artificial intelligence (AI) model for automated measure of the CE angle to aid the diagnosis of DDH by modifying the Mask R-CNN algorithm.13228 anteroposterior pelvic x-ray images were collected from the PACS system of the second Hospital of Jilin University, of which 104 images were randomly selected as test data. The rest of x-ray images were labelled and preprocessed for model development. The new AI model was the constructed based modified Mask R-CNN model to detect key points for CE angle measurement. The performance of AI model on measuring CE angle was verified by comparing with three attending orthopaedic doctors. The mean CE angles on left and right pelvis measured by the AI model was 29.46 ± 6.98°and 27.92 ± 6.56°, respectively, while the mean CE angle measured by the three doctors was 29.85 ± 6.92°and 27.75 ± 6.45°, respectively. AI model displayed a higly consistency with the doctors in measuring CE angles. Besides, AI model showed a much high efficiency in term of measuring time-consumption. In this study, we successfully constructed a new effective model for measuring CE angle by identifying key points, which provided a new intelligent measurement tool for orthopedic image measurement and evaluation.