Deep learning-based magnetic resonance imaging of the spine in the diagnosis and physiological evaluation of spinal metastases

BACKGROUND AND OBJECTIVE: Spinal metastasis accounts for 70% of the bone metastases of tumors, so how to diagnose and predict spinal metastasis in time through effective methods is very important for the physiological evaluation of the therapy of patients. METHODS: MRI scans of 941 patients with spinal metastases from the affiliated hospital of Guilin Medical University were collected, analyzed, and preprocessed, and the data were submitted to a deep learning model designed with our convolutional neural network. We also used the Softmax classifier to classify the results and compared them with the actual data to judge the accuracy of our model. RESULTS: Our research showed that the practical model method could effectively predict spinal metastases. The accuracy was up to 96.45%, which could be used to diagnose the physiological evaluation of spinal metastases. CONCLUSION: The model obtained in the final experiment can capture the focal signs of patients with spinal metastases more accurately and can predict the disease in time, which has a good application prospect.