Multimodal aneurysm rupture prediction analysis

Cerebral aneurysms are highly dangerous diseases, with their rupture leading to severe disabilities, thus necessitating accurate prediction of rupture risk. In this study, we constructed a custom dataset containing 178 aneurysms and extracted four types of features for fusion, including deep learning, clinical, morphological, and hemodynamic features. We then built five distinct machine learning models based on these selected features to analyze and predict aneurysm rupture. Specifically, the Random Forest model demonstrated the best performance in estimating the likelihood of aneurysm rupture, achieving a ROC value of 0.885. Our findings highlight that leveraging multimodal data enhances the accuracy of predicting aneurysm rupture risk, which could improve early detection and inform better clinical decision-making.