Artificial Intelligence Models for Predicting Molecular Pathway Activity in Spinal Cord Injury: A Systematic Review Publisher



Mehmandoost M ; Jabbar M ; Rahatijafarabad B ; Mehrdad M ; Tavanaei R ; Mohammadzadeh I ; Oveisi S ; Zali A ; Oraeeyazdani S ; Fahim F
Source: Interdisciplinary Neurosurgery: Advanced Techniques and Case Management Published:2026

Abstract

Background: Spinal cord injury (SCI) remains a devastating neurological condition with high global incidence and minimal curative options. Molecular signaling pathways orchestrate these responses. Although, artificial intelligence (AI) can lead to assessment of multi–dimensional omics datasets of key biomarkers, and prioritization of therapeutic targets. Methods: Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (2000–2025) and registered the protocol in PROSPERO (CRD420251115723). Inclusion criteria encompassed studies applying AI algorithms to predict or model molecular pathway activity in SCI. Risk of bias was assessed with the PROBAST tool. Results: Of 86 records, 11 studies met the criteria. Most were observational, bioinformatics-driven investigations published after 2020, predominantly from China, with heavy reliance on the GSE151371 blood transcriptomic dataset and small validation cohorts. Diagnostic and severity classification: Six studies achieved AUC values ranging 0.79–1.00; recurrent biomarkers included FCER1G, NFATC2, S100A8, and IL2RB. Mechanistic insights: Seven studies converged on immune dysregulation (NF–κB, VEGF, JAK–STAT, Toll–like receptor), novel cell death modalities (PANoptosis, cuproptosis), and metabolic/autophagy disruptions (PINK1/SQSTM1). Therapeutic predictions: Five studies proposed interventions drug repurposing (Emricasan, Alaproclate, Imatinib), cuproptosis–targeted agents, ZnO nanoparticles, and mesenchymal stem–cell transplantation. Conclusions: AI has begun mapping the intricate immune metabolic degenerative network underpinning SCI, revealing candidate biomarkers and therapeutic targets with potential regenerative relevance. However, current evidence is constrained by small, homogeneous datasets, preclinical bias, and lack of longitudinal human validation. Scaling to multicenter, multi–omics cohorts and advancing promising candidates into early–phase trials are essential next steps to realize AI translational promise in SCI management. © 2026 The Author(s)