Abstract: Abstract： The diagnosis of developmental dysplasia of the hip （DDH） using pelvic X-ray requires accurate mapping of hip key points， and deep learning methods can be used as reliable auxiliary tools. In order to solve the problem of diversified shooting posture and shooting distance for pelvic radiographs， this paper proposed RKD-UNet based on U-Net to detect keypoints of the hip. The model used residual blocks to improve U-Net’s convolution layers and skip-connection paths， as well as introduced the coordinate attention module into the encoder to enhance feature extraction ability for the keypoints neighborhood. Convolution layers and ASPP module were used on top of the encoder to form a Bridge block to fuse feature information at different scales and enhance the receptive field of the model with an atrous rate of ［3， 6， 9］. The model was trained and tested using radiographic data containing types of pelvic orthostasis， frog， full-length lower extremity， and postoperative pelvis. RKD-UNet achieves an average keypoints detection error of 3.19 ± 2.19 px and an average acetabular angle measurement error of 2.83°± 2.59°. The F1 score for the normal， mild， moderate， and severe dislocation cases were 89.6， 77.1， 57.9， and 94.1， respectively， which were higher than the doctors’ diagnostic results. Experiments have shown that RKD-UNet can accurately detect keypoints of the hip and assist doctors in diagnosing DDH.

Key words: Key words： deep learning, U-Net, keypoint detection, developmental dysplasia of hip, auxiliary diagnosis