Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.
Authors
Affiliations (2)
Affiliations (2)
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. [email protected].
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Abstract
Cerebral aneurysms are complex vascular lesions whose rupture can result in subarachnoid hemorrhage (SAH), a condition associated with substantial morbidity and mortality. Although current clinical risk assessment relies primarily on morphological characteristics such as aneurysm size and location, these factors alone are often insufficient for individualized rupture risk prediction. Advances in artificial intelligence (AI) and machine learning (ML) have enabled the integration of imaging, clinical, morphological, and computational data, creating new opportunities to improve aneurysm detection, risk stratification, and treatment planning. This systematic review evaluates recent applications of AI and ML across the cerebral aneurysm clinical pipeline, focusing on three major domains: (i) automated detection and segmentation from medical imaging, (ii) rupture risk prediction using clinical, morphological, radiomic, and computational fluid dynamics (CFD)-derived hemodynamic features, and (iii) clinical decision support for treatment planning and outcome prediction. The reviewed studies are examined with respect to their methodological approaches, input features, predictive performance, validation strategies, and clinical applicability. Across the literature, multimodal models that integrate heterogeneous data sources generally demonstrate superior predictive performance compared with approaches relying on a single feature category. However, widespread clinical implementation remains limited by retrospective study designs, heterogeneous datasets, inconsistent validation practices, limited external validation, and challenges related to model interpretability and generalizability. Emerging directions, including explainable AI, multimodal learning, and physics-informed ML, offer promising opportunities to improve model robustness and facilitate clinical translation. Overall, the reviewed evidence indicates that AI has considerable potential to support the detection, risk assessment, and management of cerebral aneurysms, while emphasizing the need for standardized datasets, prospective multicenter validation, and interpretable models to enable reliable clinical adoption.