A Systematic Review of AI Methods Across the MRI Analysis Pipeline for Multiple Sclerosis Progression Prediction

Abstract

Magnetic resonance imaging (MRI)-based prediction of multiple sclerosis (MS) progression depends on how imaging data are prepared, represented, modelled, and evaluated. This systematic review synthesized artificial intelligence (AI) methods across the MRI-to-prediction pipeline. PubMed, Scopus, Web of Science, and Google Scholar were searched for studies published from 2010 to March 2026. Eligible studies included patients with MS or clinically isolated syndrome, used brain MRI directly or as the source of predictors, evaluated future progression-related outcomes, and reported quantitative predictive performance. Thirty-seven studies were included and synthesized narratively because of methodological and outcome heterogeneity. Prediction targets included CIS-to-MS/CDMS conversion, future MRI disease activity, PIRA/PIRMA, disability worsening or confirmed progression, future EDSS or disability status, and RRMS-to-SPMS conversion. Lesion morphology and location, radiomics, tissue atrophy, longitudinal imaging changes, and multimodal variables provided useful prognostic information. Classical machine learning (ML) remained effective for structured biomarkers, while deep learning (DL) enabled direct image-based modelling; neither approach was uniformly superior. PROBAST + AI assessment rated model Development as High concern in 35 of 37 studies and Unclear in two, Evaluation as High risk of bias in all 37 studies, and Applicability as Low concern in 32, Unclear in two, and High in three. Overall, MRI-based AI shows promising prognostic potential, while stronger data separation, calibration, transparent reporting, and independent validation are needed to support reproducible clinical translation.