Using machine learning to predict the rupture risk of multiple intracranial aneurysms
Junqiang Feng1, Chunyi Wang1, Yu Wang1
1Department of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Frontiers in Neurology
|August 20, 2025
Summary
This study developed a machine learning model to predict the rupture of multiple intracranial aneurysms (MIAs), outperforming existing scores for unruptured cases but needing refinement for ruptured aneurysms.
Area of Science:
- Neurosurgery
- Medical Imaging
- Machine Learning
Background:
- Multiple intracranial aneurysms (MIAs) present unique rupture risks not addressed by current tools like the PHASES score.
- Existing risk prediction models lack specific consideration for the morphological and anatomical parameters crucial in MIAs.
Purpose of the Study:
- To develop and evaluate a machine learning-based risk prediction model (RPM) for the rupture of MIAs.
- To provide a tailored risk assessment for MIAs, improving upon general clinical tools.
Main Methods:
- A five-fold cross-validation approach was used to manage dataset imbalance.
- Model performance was assessed using accuracy, true positive rate, true negative rate, F1 score, and AUC.
Main Results:
- The model achieved an overall accuracy of 0.797 and an AUC of 0.843.
- It demonstrated higher predictive performance for unruptured aneurysms (TNR: 0.837) compared to ruptured ones (TPR: 0.644).
- Lower TPR for ruptured aneurysms was linked to small sample size and data imbalance.
Conclusions:
- The developed ML model offers a novel approach for MIA rupture risk prediction, complementing the PHASES score.
- Clinical applicability is limited by suboptimal performance in predicting ruptured aneurysms and the absence of external validation.
- Future research with larger cohorts is essential to validate and enhance the model for personalized treatment strategies.
