Abstract
The application value of the convolutional neural network (CNN) algorithm in the diagnosis of sports knee osteoarthropathy was investigated in this study. A network model was constructed in this experiment for image analysis of magnetic resonance imaging (MRI) technology. Then, 100 cases of sports knee osteoarthropathy patients and 50 healthy volunteers were selected. Digital radiography (DR) images and MRI images of all the research objects were collected after the inclusion of the two groups. Besides, the important physiological representations were extracted from their image data graphs, and the hidden complex relationships were learned. The state without input results was judged through convolutional network calculation, and the result prediction was given. On this basis, there was an analysis of the diagnostic efficiency of traditional DR images and MRI images based on CNN for patients with sports knee osteoarthropathy. The results showed that the MRI images analyzed by the CNN model showed a more obvious display rate than DR images for some nonbone changes of osteoarthritis. The correlation coefficient between MRI image rating and visual analog scale (VAS) was 0.865, which was higher than 0.713 of DR image rating, with a statistical meaning ( P < 0.01 ). For cases with mild lesions, the number of cases detected by MRI based on CNN algorithm in 0–4 image rating was 15, 18, 10, 6, and 7, respectively, which was markedly better than that of DR images. In short, the MRI examination based on the CNN image analysis model could extract important physiological representations from the image data and learn the hidden complex relationships. The convolutional network was calculated to determine the state of the uninput results and give the result predictions. Moreover, MRI examination based on the CNN image analysis model had high overall diagnostic efficiency and grading diagnostic efficiency for patients with motor knee osteoarthropathy, which was of great significance in clinical practice.
Highlights
Sports knee osteoarthropathy is a type of disease caused by degenerative lesions of the knee joints, external force injuries, overwork, and other reasons
A total of 100 patients with a clinical diagnosis of sports knee osteoarthropathy (200 knee joints), who were treated in hospital from August 2017 to December 2020, were collected as the research objects of this experiment, which were set as a lesion group, and all the patients complained of pain in the knee joint
It was found that the image features were more distinguishable after being learned by the convolutional neural network (CNN) model, and the visual feature maps extracted by the network could be fed back to the clinician, which had important research value for the doctor’s clinical diagnosis
Summary
Sports knee osteoarthropathy is a type of disease caused by degenerative lesions of the knee joints, external force injuries, overwork, and other reasons. When accurate diagnosis results are obtained, automatic calculation and extraction of characteristic information of imaging images are carried out by computer, which greatly improves work efficiency, reduces working hours, and alleviates the workload of clinicians, to avoid errors or imperfections in analysis and diagnosis caused by a heavy workload. In this experiment, the multisequence MRI image sample data of knee cartilage tissue collected were used as the training set to establish the CNN network model, and the diagnostic efficiency of the model for knee cartilage tissue and MRI images was analyzed
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