Abstract
This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.
Highlights
E diagnosis and treatment of this kind of bone structure combined with soft tissue damage are complicated
It was used to improve the quality of Magnetic resonance imaging (MRI) images of meniscal injury to analyze the application value of MRI image based on deep learning in diagnosing meniscus injury and expected to provide theoretical basis and data for follow-up related clinical operations
Reconstructed and Optimized Images Based on Deep Learning. e Regridding model image reconstruction algorithm was used to optimize the knee joint MRI undersampled images, and a fully sampled MRI image was obtained
Summary
E diagnosis and treatment of this kind of bone structure combined with soft tissue damage are complicated At this time, improper treatment or failure to find the meniscus damage in time will cause traumatic osteoarthritis in the follow-up, which will further cause severe joint dysfunction, affecting the progress of the patient’s treatment and postoperative rehabilitation. Clinical diagnosis methods for meniscus and other soft tissue injuries include arthroscopy, MRI, and knee B-ultrasound [5, 6]. Magnetic resonance imaging (MRI) has high resolution for the soft tissue of the knee joint [7] and has high specificity and sensitivity for the diagnosis of injury sites. It was used to improve the quality of MRI images of meniscal injury to analyze the application value of MRI image based on deep learning in diagnosing meniscus injury and expected to provide theoretical basis and data for follow-up related clinical operations
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