An Automatic Measurement Method of the Tibial Deformity Angle on X-Ray Films Based on Deep Learning Keypoint Detection Network

In the clinical application of the parallel external fixator, medical practitioners are required to quantify deformity parameters to develop corrective strategies. However, manual measurement of deformity angles is a complex and time-consuming process that is susceptible to subjective factors, resulting in nonreproducible results. Accordingly, this study proposes an automatic measurement method based on deep learning, comprising three stages: tibial segment localization, tibial contour point detection, and deformity angle calculation. First, the Faster R-CNN object detection model, combined with ResNet50 and FPN as the backbone, was employed to achieve accurate localization of tibial segments under both occluded and nonoccluded conditions. Subsequently, a relative position constraint loss function was added, and ResNet101 was used as the backbone, resulting in an improved RTMPose keypoint detection model that achieved precise detection of tibial contour points. Ultimately, the bone axes of each tibial segment were determined based on the coordinates of the contour points, and the deformity angles were calculated. The enhanced keypoint detection model, Con_RTMPose, elevated the Percentage of Correct Keypoints (PCK) from 63.94% of the initial model to 87.17%, markedly augmenting keypoint localization precision. Compared to manual measurements conducted by medical professionals, the proposed methodology demonstrates an average error of 0.52°, a maximum error of 1.15°, and a standard deviation of 0.07, thereby satisfying the requisite accuracy standards for orthopedic assessments. The measurement time is approximately 12 s, whereas manual measurement requires about 15 min, greatly reducing the time required. Additionally, the stability of the models was verified through K-fold cross-validation experiments. The proposed method meets the accuracy requirements for orthopedic applications, provides objective and reproducible results, significantly reduces the workload of medical professionals, and greatly improves efficiency.