Review uncovers advances in biomarkers for post-traumatic epilepsy, suggesting AI-driven methods may enhance early diagnosis.
Early diagnosis of post-traumatic epilepsy (PTE) is crucial for timely intervention. However, it is hampered by the lack of reliable biomarkers. In this review, we provide a comprehensive summary of current advances in PTE biomarker research, drawing primarily on evidence from human cohort studies, with selective support from experimental animal models where mechanistic insights are required. We cover (i) neuroimaging, including CT, MRI, and EEG/qEEG, which reveal structural and functional alterations associated with epileptogenesis; (ii) molecular biomarkers, including RNAs, proteins, metabolites, and extracellular vesicle (EV)-derived molecules that reflect neuroinflammation, blood–brain barrier (BBB) dysfunction, neuronal injury, and synaptic remodeling; and (iii) artificial intelligence (AI)-assisted approaches, which integrate multimodal datasets to identify complex predictive patterns. While individual modalities offer valuable but incomplete prognostic information, AI-driven analytics hold the greatest promise for early predictive power by combining multimodal data. Future progress will depend on the integration of high-resolution imaging, multi-omics profiling, and rigorous validation to deliver clinically actionable biomarker panels and ultimately reduce the burden of PTE.
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Demoz et al. (2026) studied this question.
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