Artificial Intelligence for Intracranial Aneurysm Detection, Rupture-Risk Prediction, and Prognosis Evaluation: Recent Advances and Clinical Integration

Abstract

Intracranial aneurysm rupture is a major cause of hemorrhagic stroke and a significant contributor to human mortality. With the emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), a series of new approaches have been introduced to improve aneurysm detection, rupture assessment, risk prediction, and prognosis evaluation. This narrative review synthesizes the findings from 21 peer-reviewed studies identified through searches in PubMed, Scopus, Web of Science, and IEEE Xplore, covering the period from June to September 2025. The reviewed studies examined the use of AI for aneurysm detection, rupture assessment and risk prediction, and prognosis evaluation based on clinical, morphological, radiomic, and hemodynamic data. The reviewed studies applied different AI approaches, including conventional ML algorithms, convolutional neural networks, transformer-based models, radiomics, and multimodal models. Several studies reported encouraging performance in aneurysm detection, segmentation, rupture assessment, and prognosis evaluation, while some approaches showed better performance compared with traditional clinical scoring systems. However, important limitations remain, including the frequent use of small or single-center datasets, limited external validation, and heterogeneity in imaging protocols and feature-extraction methods. AI is showing strong potential in supporting the management of intracranial aneurysms. However, further research is needed, particularly through multicenter and external validation, improved model explainability and robustness, and better integration of these systems into clinical workflows.