Deep Learning-Based Magnetic Resonance Imaging Image Features for Diagnosis of Anterior Cruciate Ligament Injury

Abstract

To study and explore the adoption value of magnetic resonance imaging (MRI) in the diagnosis of anterior cruciate ligament (ACL) injuries, a multimodal feature fusion model based on deep learning was proposed for MRI diagnosis. After the related performance of the proposed algorithm was evaluated, it was utilized in the diagnosis of knee joint injuries. Thirty patients with knee joint injuries who came to our hospital for treatment were selected, and all patients were diagnosed with MRI based on deep learning multimodal feature fusion model (MRI group) and arthroscopy (arthroscopy group). The results showed that deep learning-based MRI sagittal plane detection had a great advantage and a high accuracy of 96.28% in the prediction task of ACL tearing. The sensitivity, specificity, and accuracy of MRI in the diagnosis of ACL injury was 96.78%, 90.62%, and 92.17%, respectively, and there was no considerable difference in contrast to the results obtained through arthroscopy ( P > 0.05 ). The positive rate of acute ACL patients with bone contusion and medial collateral ligament injury was substantially superior to that of chronic injury. Moreover, the incidence of chronic injury ACL injury with meniscus tear and cartilage injury was notably higher than that of acute injury, with remarkable differences ( P < 0.05 ). In summary, MRI images based on deep learning improved the sensitivity, specificity, and accuracy of ACL injury diagnosis and can accurately determined the type of ACL injury. In addition, it can provide reference information for clinical treatment plan selection and surgery and can be applied and promoted in clinical diagnosis.

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

“…Knee bone and joint diseases are ubiquitous in almost all groups of age and sex. These are anterior cruciate ligament (ACL) injuries, osteoarthritis (OA), and osteoporosis (OP) [ 1 – 3 ]. The knee joint comprises the femur, tibia, patella, and the synovial membrane, which contains synovial fluid.…”

An Improved Neural Network Model for Early Detection of Joint Injuries in Tai Chi Sports

“…e initial step in this study was to rebuild the input data format. Specifically, this research reconstructs the image data into a two-dimensional input feature map of the kind described in [30], which can then be used to adapt the enhanced CNN network model input data format for effective convolution and downsampling operations.…”

“…A dense neural network approach which uses multiple layers for feature extraction was used for the diagnosis problem of knee osteoarthritis classification in elderly people [29]. Another research on convolutional neural networks for the task of initial knee MRI diagnosis was able to locate tears in knees without localization information [30]. Another method for detecting knee joints and quantifying knee osteoarthritis severity was carried out using convolutional neural networks.…”

Detection Method of Athlete Joint Injury Based on Deep Learning Model

“…The application of deep learning in knee joint MRI image analysis is becoming a hot research content. Li et al [17] used the multimode feature fusion model in deep learning to diagnose the injury of knee joint MRI images. The results show that the prediction accuracy of the model in knee joint tear is 96.28%, and it is proved that the MRI image classification model based on depth learning can accurately classify the type of anterior cruciate ligament injury.…”