Intelligent diagnostic method for developmental hip dislocation - Sun, Hang; Li, Hong; Zhao, Yuhang; Pan, Shinong (Frontiers in Physics, 2024)
Background: Developmental hip dislocation is a disease that severely threatens children's healthy growth. Without timely and correct treatment, it will lead to osteoarthritis and hip dysfunction in the evolution of children. Objective: It is essential to realize an intelligent model to judge hip dislocation and accurate quantitative analysis. Methods: In this paper, 46 cases of CT images were retrospectively collected, including 19 cases of hip dislocation and 27 cases of healthy people. The experiment first uses ITK-SNAP to sketch the ilium and femoral head in the original image. Then it uses a 3D U-Net to send the label of the background, ilium, and femoral head into three channels, respectively, to realize the threedimensional segmentation of the ilium and femoral head. Next, the extraction of the surface of the acetabulum and femoral head is performed. Subsequently, the erroneous points are eliminated, and the spherical surfaces of the acetabulum and femoral head are fitted using the least squares method.Ultimately, the spherical center distance is calculated quantitatively to predict whether the hip joint is dislocated. Results: Under the independent test set, the segmentation average dice coefficients of the ilium and femoral head are 89% and 93%, respectively. The spherical center distance between the acetabulum and femoral head is calculated quantitatively. If the value exceeds 10mm, it is considered a hip dislocation. Compared with the doctor's diagnosis, the accuracy result is 94.4%. Conclusions: This paper successfully implements an precise and automated intelligent diagnostic system for the identification of hip dislocation. Commencing with the development of a 3D segmentation algorithm for the ilium and femoral head, we further introduce a novel method that computes the spherical distance for the prediction of hip dislocation. This approach provides robust quantitative analysis, thereby facilitating more informed clinical decision-making.