Deep-Learning-Based MRI Images for Analysis of Sport-Induced Ankle Joint Injury

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.