Predictive Performance of Machine Learning with Evoked Potentials for SCI and MS Prognosis: A Meta-Analysis

Abstract

Evoked potentials (EPs), including somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs), are used to assess neural conduction in spinal cord injury (SCI) and multiple sclerosis (MS), conditions marked by demyelination, inflammation, and axonal damage. Machine learning (ML), using data-driven algorithms, enhances EPs’ prognostic utility, but evidence synthesis is limited. This meta-analysis evaluated the predictive performance of EP-based ML models for SCI recovery (ASIA scale) and MS progression (EDSS) using a random-effects model. Five studies (n = 583) were included, extracting accuracy and area under the curve (AUC). Pooled results showed high predictive accuracy of 77.7% (95% CI, 75.1–80.3%; I² = 57%) and AUC 0.82 (95% CI, 0.79–0.85; I² = 55%). Stratified analyses by disease type (SCI vs. MS) or injury severity were not feasible due to the limited number of studies (n = 5). Sensitivity analysis excluding a rat model (N = 551) showed stable results (accuracy 76.9%; AUC 0.81). SSEP latency and MEP time series were key predictors, with amplitude critical in SCI and multimodal approaches enhancing performance. Moderate heterogeneity (I² = 55–57%) and limited studies constrain generalizability. This meta-analysis highlights EPs’ prognostic potential in ML-driven precision neurology, advocating for further human studies to validate multimodal approaches.

Keywords

• machine learning
• evoked potentials
• somatosensory evoked potentials
• motor evoked potentials
• spinal cord injury
• multiple sclerosis