Investigating the role of blood models in predicting rupture status of intracranial aneurysms

Abstract

Purpose. Selecting patients with high-risk intracranial aneurysms (IAs) is of clinical importance. Recent work in machine learning-based (ML) predictive modeling has demonstrated that lesion-specific hemodynamics within IAs can be combined with other information to provide critical insights for assessing rupture risk. However, how the adoption of blood rheology models (i.e., Newtonian and Non-Newtonian blood models) may influence ML-based predictive modeling of IA rupture risk has not been investigated.Methods and Materials.In this study, we conducted transient CFD simulations using Newtonian and non-Newtonian rheology (Carreau-Yasuda [CY]) models on a large cohort of 'patient-specific' IA geometries (>100) under pulsatile flow conditions to investigate how each blood model may affect the characterization of the IAs' rupture status. Key hemodynamic parameters were analyzed and compared, including wall shear stress (WSS) and vortex-based parameters. In addition, velocity-informatics features extracted from the flow velocity were utilized to train a support vector machine (SVM) model for rupture status prediction.Results.Our findings demonstrate significant differences between the two models (i.e., Newtonian versus CY) regarding the WSS-related metrics. In contrast, the parameters derived from the flow vortices and velocity informatics agree. Similar to other studies, using a non-Newtonian CY model results in lower peak WSS and higher oscillatory shear index (OSI) values. Furthermore, integrating velocity informatics and machine learning achieved robust performance for both blood models (area under the curve [AUC] ˃0.85).Conclusions.Our preliminary study found that ML-based rupture status prediction derived from velocity informatics and geometrical parameters yielded comparable results despite differences observed in aneurysmal hemodynamics using two blood rheology models (i.e., Newtonian versus CY).

Keywords: hemodynamics; machine learning; non-Newtonian.