Abstract
The aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize it, and the magnetic resonance imaging (MRI) image super-resolution reconstruction model (SRCNN) was established. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were compared between SRCNN and other algorithms. Sixty patients with meniscus injury of knee joint were studied. Arthroscopic surgery was performed according to the patients' actual type of injury, and knee scores were evaluated for all patients. Then, postoperative scores and MRI results were analyzed. The results showed that the PSNR and SSIM values of the SRCNN algorithm were (42.19 ± 4.37) dB and 0.9951, respectively, which were significantly higher than those of other algorithms (P < 0.05). Among patients with meniscus injury, 17 cases (28.33%) were treated with meniscus suture, 39 cases (65.00%) underwent secondary resection, 3 cases (5.00%) underwent partial resection, and 1 case (1.67%) underwent full resection. After meniscus suture, secondary resection, partial resection, and total resection, the knee function scores of patients after treatment were (83.17 ± 8.63), (80.06 ± 7.96), (84.34 ± 7.74), and (85.52 ± 5.97), respectively. There was no great difference in knee function scores after different methods of treatment (P > 0.05), and there were considerable differences compared with those before treatment (P < 0.01). Compared with the results of arthroscopy, there was no significant difference in the grading of meniscus injury by MRI (P > 0.05). To sum up, the SRCNN algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries. Arthroscopic knee surgery had good results and had great clinical application and promotion value.
Highlights
Academic Editor: Enas Abdulhay e aim of this study is to explore the clinical effect of deep learning-based magnetic resonance imaging (MRI)-assisted arthroscopy in the early treatment of knee meniscus sports injury
The super-resolution reconstruction model (SRCNN) algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries
MRI Diagnosis Result of Meniscus Injury Degree. e results of arthroscopy or intraoperative exploration were used as the gold standard to evaluate the accuracy of MRI in the diagnosis of meniscus injury (Figure 7). ere were 19 cases (31.67%), 34 cases (56.67%), and 7 cases (11.67%) of meniscus injury diagnosed by arthroscopy as grades I, II, and III, respectively. ere were 20 cases (33.33%), 27 cases (45.99%), and 13 cases (21.67%) of meniscus injuries diagnosed by MRI as grades I, II, and III, respectively. ere was no obvious difference in the grading of meniscus injury between results of MRI and arthroscopy (P > 0.05)
Summary
Academic Editor: Enas Abdulhay e aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize it, and the magnetic resonance imaging (MRI) image super-resolution reconstruction model (SRCNN) was established. Secondary resection, partial resection, and total resection, the knee function scores of patients after treatment were (83.17 ± 8.63), (80.06 ± 7.96), (84.34 ± 7.74), and (85.52 ± 5.97), respectively. The SRCNN algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries. Meniscus injuries are often diagnosed by arthroscopy, knee ultrasound, CT, and magnetic resonance imaging (MRI). Kobayashi et al [8] pointed out that the image resolution processed by the three-layer convolutional neural network (CNN) (super-resolution CNN, SRCNN) was significantly improved. SRCNN based on deep CNN algorithm should be further optimized to increase its image quality
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