ORIGINAL RESEARCH article
Front. Neurol.
Sec. Endovascular and Interventional Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1539341
Using Machine Learning to Predict the Rupture Risk of Multiple Intracranial Aneurysms
Provisionally accepted
- 1Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- 2Beijing Institute for Brain Disorders, Capital Medical University, Beijing, Beijing Municipality, China
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Objective: This 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.To 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).Results: Ninety-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.Our 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.Significance: Multiple 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
Keywords: Multiple intra cranial aneurysms, machine learning, Risk factors, Subarachnoid Hemorrhage, Risk prediction model
Received: 04 Dec 2024; Accepted: 16 Jul 2025.
Copyright: © 2025 Wang, Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: He Liu, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.