Ai-enabled multimodal analysis enhances detection of motor neuron disease pathways

Abstract Motor neuron disease (MND) is widely recognized for confounding clinicians due to complex etiologies, variable progression patterns, and incomplete mechanistic clarity that hamper early diagnosis. Evidence was drawn from 89 peer-reviewed manuscripts. A systematic approach was adopted to gather them from major scientific libraries, then details were synthesized through structured classification of multi-omics, imaging-based data, together with novel computational frameworks. Novel cross-domain data integration was proposed to reveal hidden molecular interactions in MND pathogenesis. Major findings indicated that advanced analytics, when combined with diverse biomarkers, can isolate subtle disease signals not detected by conventional methods. Machine-based approaches, although increasingly employed, seldom incorporate multi-omics strategies nor robust interpretability measures. Additional attention was identified for bridging imaging-derived characteristics with transcriptomic, proteomic, and epigenetic markers under uniform pipelines. Proposed contributions included ethical data curation protocols and cross-validation to address sample variability, in addition to large-scale collaboration for reproducible outcomes. This review offers an in-depth summary of existing trends, addresses data harmonization concerns, and specifies future directions for integrative MND research.