A Learning-based Correlated Graph Model for Spinal Cord Injury Prediction from Magnetic Resonance Spinal Images

Abstract

In epidemiological research on spine surgery, machine learning represents a promising new area. It is made up of several algorithms that work together to identify patterns in the data. Machine learning provides many benefits over traditional regression techniques, including a lower necessity for a priori predictor information and a higher capacity for managing huge datasets. Recent research has made significant progress toward using machine learning more effectively in spinal cord injury (SCI). Machine learning algorithms are employed to analyze non-traumatic and traumatic spinal cord injuries. Non-traumatic spinal cord injuries often reflect degenerative spine conditions that cause spinal cord compression, such as degenerative cervical myelopathy. This article proposes a novel correlated graph model (CGM) that adopts correlated learning to predict various outcomes published in traumatic and non-traumatic SCI. In the studies mentioned, machine learning is used for several purposes, including imaging analysis and epidemiological data set prediction. We discuss how these clinical predictive models are based on machine learning compared to traditional statistical prediction models. Finally, we outline the actions that must be taken in the future for machine learning to be a more prevalent statistical analysis method in SCI.