Mechanisms and integrative machine learning approaches to blood–brain barrier biomarker profiling for personalized ischemic stroke management

Abstract

Ischemic stroke remains a leading cause of death and disability worldwide, with blood-brain barrier (BBB) disruption playing a central role in vasogenic edema, neuroinflammation, hemorrhagic transformation, and secondary neuronal injury. The BBB is a specialized neurovascular unit composed of endothelial tight junctions, pericytes, astrocytes, and basement membrane structures that undergo coordinated molecular and cellular changes during ischemia-reperfusion injury, generating diverse biomarker signatures including endothelial dysfunction, oxidative stress, inflammatory mediators, and extracellular matrix remodeling. However, conventional biomarkers and imaging approaches fail to fully capture the dynamic and heterogeneous nature of BBB injury. Meaningful interpretation of BBB-derived biomarkers requires mechanistic understanding of their molecular and cellular origins, making the integration of BBB pathophysiology with computational modeling essential for clinically relevant translation. Recent advances in machine learning (ML) and deep learning (DL) enable integration of neuroimaging, molecular, clinical, and multi-omics data to characterize BBB dysfunction and improve prediction of stroke outcomes. ML-based models have demonstrated value in identifying BBB-related signatures associated with infarct progression, hemorrhagic transformation, and functional recovery, while deep neural networks enhance lesion segmentation and prognostic modeling. Despite this progress, challenges including data heterogeneity, limited longitudinal datasets, and model interpretability remain barriers to clinical translation. This review integrates the molecular and cellular mechanisms of BBB disruption with machine learning approaches for BBB biomarker profiling, highlighting a pathway toward biologically informed, personalized ischemic stroke management.