Abstract
This study was to analyze the sport-induced ankle joint injury (AJI) images based on the neural network algorithms using the magnetic resonance imaging (MRI). 20 patients and 20 volunteers were included in the experimental and control groups, respectively. The hybrid diffusion equation (HDE) neural network (HDENN) algorithm was compared with the fully convolutional neural network (FCNN) and the FCNN preprocessing, and the HDE was applied to the MRI analysis of sport-induced AJI. The results showed that the total score of MRI image for the conventional position of the anterior talofibular ligament (ATFL) and posterior talofibular ligament (PTFL) was concentrated in 4 (55%) and 5 (65%), respectively. The number of patients with good prognosis with grade II injury (11 cases) was much higher than that of grade III injury (2 cases), and the number of patients with poor prognosis (4 cases) was lower than that of grade III injury (6 cases) ( P < 0.05 ). Conventional MRI was recommended to observe the ATFL and PTFL, and the valgus position MRI was recommended for the calcaneofibular ligament (CFL); conservative treatment was recommended for patients with grades I and II AJI, but surgical treatment was recommended for patients with grade III AJI.
Highlights
Located between the lower leg and the foot of the human body, the ankle joint mainly undertakes the weight-bearing function of the human body during exercise
E hybrid diffusion equation (HDE) neural network algorithm proposed in this study was compared with fully convolutional neural network (FCNN) and FCNN preprocessing for effective comparison and analysis, and it was applied to the magnetic resonance imaging (MRI) image analysis of sport-induced ankle joint injury (AJI). e objective of studying AJI and pathological grading was to detect whether MRI images can effectively assess motor AJI and provide a simple and efficient diagnosis method for the clinic
Comparison on Segmentation Performance of ree Algorithms. e results in Figure 2 illustrated that the performance of the HDENN algorithm was not greatly improved compared with FCNN and FCNN preprocessing in the complete area, and the evaluation index was only improved by 0.01
Summary
Located between the lower leg and the foot of the human body, the ankle joint mainly undertakes the weight-bearing function of the human body during exercise. It mainly relies on the cooperation of ligaments and tendons to maintain its stability [1]. The most common AJIs include joint ligament injury, dislocation, and fracture [3]. Most AJIs are caused by fixed and rapid foot movement, sudden inversion, and adduction of the ankle joint, causing ligament tears and injuries and severe cases with ankle fractures [4]. If the treatment is not performed in time, or the treatment is not thorough, it will cause repeated sprains in the future and seriously affect the normal function of the joints. erefore, it is necessary for clinical medical staff to master anatomical knowledge of ankle joints, find the ankle joint and surrounding ligament injuries in time, and adopt corresponding treatment measures
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