Improving Prediction of Intracranial Aneurysm Rupture Status Using Temporal Velocity-Informatics

This study uses a spatial pattern analysis of time-resolved aneurysmal velocity fields to enhance the characterization of intracranial aneurysms' (IA) rupture status. We name this technique temporal velocity-informatics (TVI). In this study, using imaging data obtained from 112 subjects harboring IAs with known rupture status, we reconstructed 3D models to get aneurysmal velocity data by performing computational fluid dynamics (CFD) simulations and morphological information. TVI analyses were conducted for time-resolved velocity fields to quantitatively obtain spatial and temporal flow disturbance. Lastly, we employed four machine learning (ML) methods (e.g., support vector machine [SVM]) to evaluate the prediction performance of the proposed TVI. Overall, the SVM's prediction with TVI performed the best: an area under the curve (AUC) value of 0.92 and a total accuracy of 86%. With TVI, the SVM classifier correctly identified 77 and 92% of ruptured and unruptured IAs, respectively.