Machine Learning Introduces Electrophysiology Assessment as the Best Predictor for the Recovery Prognosis of Spinal Cord Injury Patients for Personalized Rehabilitation Approaches

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning.