The roles of radiomics and deep learning for automatic detection, stability assessment, and rupture risk prediction in intracranial aneurysms: a systematic review

The models based on radiomics and deep learning for the automatic detection, stability assessment, and rupture risk prediction of IAs achieve excellent performance, and may be a potential non-invasive tool to help the clinical stratification management of high-risk IAs patients. However, current studies are predominantly retrospective, single-center, and heterogeneous, with limited calibration and external validation. Hence, there is a critical need for large-scale, prospective, long-term follow-up and multicenter studies to further establish the roles of these techniques. Future research should focus on building an easy-to-use and open-source dynamic online tool that combines standardized and normalized multi-center IAs radiomics systems with interpretable deep learning methods to enhance the accuracy and reliability of IAs risk assessment and management.