Abstract
Introduction: this study addressed the emerging role of neurovisualization in assessing and mitigating stroke risk among athletes, a population increasingly exposed to neurovascular stress due to intense physical activity. the subject gained relevance with the growing recognition of subclinical cerebrovascular changes in sports contexts. Objective: the objective of the research was to explore the integration of advanced neuroimaging techniques and artificial intelligence to enhance early detection, longitudinal monitoring, and risk prediction of cerebrovascular events in athletic populations. Methodology: a systematic literature review was conducted, focusing on studies that applied functional magnetic resonance imaging, diffusion tensor imaging, computed tomography angiography, and other modalities in combination with predictive analytics and machine learning models. inclusion criteria were applied to filter relevant research involving athletes and cerebrovascular monitoring. Results: the main results indicated that imaging biomarkers such as microbleeds, perfusion deficits, and white matter disruptions could be effectively detected and interpreted using artificial intelligence models. wearable data integrated with neuroimaging further enhanced the precision of predictive assessments. Discussion: the findings were consistent with previous studies that supported the use of multimodal imaging and computational tools in stroke risk evaluation. however, data heterogeneity and algorithmic transparency were identified as persistent challenges across the reviewed literature. Conclusions: It is concluded that the integration of neurovisualization with predictive analytics offers a promising framework for proactive brain health management in athletes and should be further developed and standardized in clinical practice.