Machine Learning for Multiple Sclerosis: A Systematic Review of Data and Methodologies

Abstract

Multiple Sclerosis (MS) is a chronic neurological disorder of the central nervous system. Its diagnosis and monitoring rely heavily on Magnetic Resonance Imaging (MRI), while increasingly complemented by other imaging, signal-based, gait-related, and clinical data sources. Manual interpretation is labor-intensive and variable, driving interest in machine learning (ML) approaches for automated lesion segmentation, classification, and detection across MRI and complementary modalities. This systematic review, conducted under PRISMA guidelines, consolidates recent advances in ML for MS diagnosis and monitoring and proposes a task-based taxonomy linking clinical objectives to methodological families. The analysis reveals that most studies rely solely on structural MRI, with limited exploration of multimodal or longitudinal data, including combinations with optical coherence tomography, retinal imaging, electroencephalography, gait analysis, plantar pressure, and clinical variables. The Dice Similarity Coefficient (DSC) dominates evaluation, with reported scores reaching up to 0.981. Results are benchmarked on public datasets such as ISBI 2015 and MICCAI 2016, and the review critically examines reproducibility barriers posed by small or private cohorts. Despite notable progress, key bottlenecks—dataset scarcity, class imbalance, domain shift, and inconsistent evaluation—continue to hinder generalization and clinical adoption. Computational costs and the absence of standardized pipelines further delay translation. Emerging strategies are also highlighted, including federated learning for privacy-preserving training, few-shot learning for data-efficient modeling, multimodal integration, and explainable AI to support transparency and clinician trust in comprehensive MS analysis. By unifying methodological insights into a structured taxonomy, benchmarking progress across datasets, and charting pathways to clinical deployment, this review provides a comprehensive roadmap toward building robust, data-efficient, and trustworthy ML systems for clinical decision support in MS.