A Deep Learning Approach for Spine Cervical Injury Severity Determination through Axial and Sagittal Magnetic Resonance Imaging Segmentation and Classification

Abstract

Abstract Objectives: Spinal cord injuries (SCI) require extensive efforts to predict the outcome of patients. While the ASIA Impairment Scale is the gold standard to assess patients with SCI, it has some limitations due to the subjectivity and impracticality in certain cases. Recent advances in machine learning (ML) and image recognition have prompted research into using these tools to predict outcomes. The aim of this study is to present a comprehensive analysis using deep learning techniques to evaluate and predict cervical spine injuries from MRI scans. Materials & Method: This is a cross-sectional database study, with patients admitted with traumatic and nontraumatic cervical SCI from 2019 to 2022 were included in our study. MRI images were labelled by four senior resident physicians. We trained a deep convolutional neural network using axial and sagittal cervical MRI images from our dataset and assessed model performance. Result: In the axial spinal cord segmentation, we achieved a dice score of 0.94 for and IoU score of 0.89. In the sagittal spinal cord segmentation, we obtained a dice scores up to 0.9201 and IoU scores up to 0.8541. The model for axial image score classification gave a satisfactory result with an f1 score of 0.72 and AUC of 0.79. Conclusion: Deep learning has been used in automated diagnostic tools, showing promise for significant future advancement. Our models were effectively able to identify cervical spinal cord injury on T2-weighted MR images with satisfactory performance. Further research is necessary to create an even more advanced model for predicting patient outcomes in spinal cord injury cases.