AUTHOR=Feng Junqiang , Wang Chunyi , Wang Yu , Liu He 

TITLE=Using machine learning to predict the rupture risk of multiple intracranial aneurysms

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1539341

DOI=10.3389/fneur.2025.1539341

ISSN=1664-2295

ABSTRACT=ObjectiveThis study aims to develop a machine learning-based risk prediction model (RPM) for the rupture of multiple intracranial aneurysms (MIAs), addressing a critical gap in current clinical tools such as the PHASES score, which are not specifically designed for MIAs. By analyzing detailed morphological and anatomical parameters, our model provides a tailored approach to rupture risk assessment in MIAs, offering potential improvements over existing methods.MethodsTo address dataset imbalance, we conducted five-fold cross-validation. External validation was not feasible due to data limitations, but we rigorously evaluated model performance using metrics such as accuracy (ACC), true positive rate (TPR), true negative rate (TNR), F1 score, and area under the receiver operating characteristic curve (AUC).ResultsNinety-one patients with 222 aneurysms were recruited, with a rupture rate of 20.3%. The model demonstrated preferable predication performance in unruptured aneurysms (TNR: 0.837) but showed limitations in predicting ruptured aneurysms (TPR: 0.644). Error analysis revealed that the model’s lower TPR may be attributed to the small sample size and dataset imbalance. Overall, the model achieved an accuracy of 0.797 and an AUC of 0.843.ConclusionOur model provides a novel approach to predicting rupture risk in MIAs, complementing existing tools like the PHASES score. However, its clinical applicability is currently limited by suboptimal performance for ruptured aneurysms, which is more suited for identifying MIAs after rupture rather than predicting future rupture risk, and the lack of external validation. Future studies with larger, prospective cohorts are needed to validate and refine the model. This work highlights the potential of machine learning to enhance rupture risk assessment in MIAs, offering a foundation for more personalized treatment strategies.SignificanceMultiple intracranial aneurysms have distinct mechanisms of formation, progression, and rupture. The widely used PHASES score does not incorporate morphological parameters of aneurysms and is not specifically designed for patients with multiple aneurysms. Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.