Advances and Applications of Artificial Intelligence in Cerebrovascular Diseases

Cerebrovascular diseases, such as stroke, aneurysm, and vascular malformations, remain major contributors to global morbidity and mortality. Their complex pathophysiology and diverse subtypes demand precise diagnosis, risk assessment, and personalized treatment strategies. However, conventional diagnostic and therapeutic approaches are hampered by subjectivity, variable accuracy, and limited scalability, emphasizing the urgent need for objective, efficient, and reliable solutions in clinical practice.Artificial intelligence (AI) advancements—especially in deep learning, imaging analytics, and multimodal data integration—have enabled significant progress in lesion detection, risk prediction, and clinical decision support for cerebrovascular disease. While promising results have been achieved in both research and pilot clinical deployments, critical challenges persist, including limited generalizability of algorithms, difficulties in multi-center data validation, and the absence of unified standards and ethical guidelines. Bridging these gaps is key to transitioning AI from experimental settings to routine clinical use.This Research Topic aims to accelerate the translation and integration of artificial intelligence into the management of cerebrovascular diseases. Our goal is to showcase interdisciplinary work that advances early detection, precise diagnosis, risk stratification, treatment optimization, and outcome prediction, promoting scalable and standardized AI applications in real-world practice.Research is welcomed within the domains of AI development, evaluation, and implementation for cerebrovascular diseases; studies must clearly address the intersection of AI and relevant clinical or research needs. To gather further insights, we seek articles addressing, but not limited to, the following themes:· AI-driven early detection and screening· Automated imaging analysis· Machine learning-based risk and outcome prediction· AI-powered decision-support and precision therapy· Data interoperability, standardization, and ethical considerations· Multi-omics and multimodal integration Topic editors: Chuanming Li, Jingzhen He, Zhifeng Kou Source: https://www.frontiersin.org/research-topics/79133/advances-and-applications-of-artificial-intelligence-in-cerebrovascular-diseases