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 29.466.98and 27.926.56, respectively, while the mean CE angle measured by the three doctors was 29.856.92and 27.756.45, 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.

髋关节发育不良（DDH）是一种常见的骨科疾病。迫切需要一种用于辅助 DDH 早期诊断的简单且具有成本效益的科学工具。本研究通过修改Mask R-CNN算法，提出了一种新的人工智能（AI）模型，用于自动测量CE角度，以辅助诊断DDH。从第二医院的PACS系统收集了13228张骨盆前后位X射线图像吉林大学，其中随机选取 104 张图像作为测试数据。其余的 X 射线图像被标记和预处理用于模型开发。新的 AI 模型是基于改进的 Mask R-CNN 模型构建的，用于检测 CE 角度测量的关键点。通过与三位主治骨科医生进行比较，验证了 AI 模型在测量 CE 角方面的性能。AI模型测量的左右骨盆平均CE角分别为29.466.98和27.926.56，而三位医生测量​​的平均CE角分别为29.856.92和27.756.45。AI 模型在测量 CE 角度时与医生高度一致。此外，人工智能模型在测量时间消耗方面表现出非常高的效率。本研究通过关键点识别成功构建了一种新的有效的CE角测量模型，为骨科影像测量和评估提供了一种新的智能测量工具。AI模型在测量时间消耗方面表现出非常高的效率。本研究通过关键点识别成功构建了一种新的有效的CE角测量模型，为骨科影像测量和评估提供了一种新的智能测量工具。AI模型在测量时间消耗方面表现出非常高的效率。本研究通过关键点识别成功构建了一种新的有效的CE角测量模型，为骨科影像测量和评估提供了一种新的智能测量工具。