Neurocysticercosis in the Era of Artificial Intelligence: Neuroanatomical Insights, Neuroimaging and Predictive Computational Neurology

Abstract

Neurocysticercosis (NCC), caused by the larval stage of Taenia solium, remains the most common parasitic infection of the central nervous system and a leading cause of acquired epilepsy worldwide. Despite substantial advances in neuroimaging, diagnosis and prognostic assessment remain challenging because of the heterogeneous clinical presentation and variable radiological features of the disease. Emerging developments in artificial intelligence (AI), radiomics, connectomics, and predictive computational neurology offer novel opportunities to improve diagnostic accuracy and individualised patient management. A structured literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed studies published between 2000 and 2025. The retrieved evidence was synthesised in a comprehensive narrative review focusing on the pathophysiology, neuroanatomical manifestations, neuroimaging characteristics and emerging computational applications in neurocysticercosis. Current evidence confirms the central role of computed tomography and magnetic resonance imaging in the diagnosis and staging of neurocysticercosis. Recent advances in radiomics, connectomics, machine learning, and deep learning demonstrate promising potential for improving lesion characterisation, differential diagnosis, quantitative imaging analysis, and prediction of neurological outcomes. Although disease-specific AI applications remain limited, computational methodologies successfully applied in neuroradiology, epilepsy, and neuro-oncology may be adapted to neurocysticercosis, supporting the development of predictive computational neurology and precision medicine. Neurocysticercosis represents a promising model for the future integration of neuroimaging, computational neuroscience, and artificial intelligence. However, current evidence remains limited by the scarcity of disease-specific datasets and the lack of large-scale clinical validation. Future multicenter studies integrating radiomics, connectomics, explainable AI, and standardised neuroimaging protocols will be essential for translating computational advances into clinically applicable diagnostic and prognostic tools capable of improving individualised neurological care.