Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

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.

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“…AI has also been proven to be helpful in detecting soft-tissue injuries around the knee. A study by Xie et al has shown that magnetic resonance imaging scans augmented with AI were able to detect meniscal defects combined with tibial plateau fractures with an accuracy of 95 % and the features were very consistent with intraoperative findings [ 12 ].…”

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“…AI has also shown promising results in several other diagnostic applications, ranging from developmental abnormalities to soft-tissue knee injuries. A proof-of-concept investigation by Xie et al (2021) [15] tested a CNN-based algorithm to improve the quality of MRI scans in tibial plateau fractures with combined meniscal defects [16]. The authors documented a sensitivity of 96.9%, specificity of 93.2%, and accuracy of 95.3%, respectively, when MRI diagnostics were compared with arthroscopic findings.…”

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“…AI has also shown promising results in several other diagnostic applications, ranging from developmental abnormalities to soft-tissue knee injuries. A proof-of-concept investigation by Xie et al 42 tested a CNN-based algorithm to improve the quality of MRI scans in tibial plateau fractures with combined meniscal defects. 43 The authors documented a sensitivity of 96.9%, specificity of 93.2%, and accuracy of 95.3%, respectively, when MRI diagnostics were compared with arthroscopic findings.…”

“…This study is one of many that highlights feasible grounds for future research and advancements for current imaging modalities. 42 Regarding congenital abnormalities, such as hip dysplasia, studies have also shown practicalities for radiological measurements in a quick and effective manner. 44 AI-assisted diagnosis and classification of OA from radiographs have demonstrated similar accuracy to senior clinicians.…”