Automated hemodynamic modeling to explore arterial curvature effects on intracranial aneurysm initiation

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Adi Konsens, Alejandro F Frangi, Gil Marom

Published: 01 Apr 2026, Last Modified: 23 Jan 2026Computer Methods and Programs in BiomedicineEveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: BACKGROUND AND OBJECTIVE: Intracranial aneurysms (IA) cause hundreds of thousands of deaths annually, yet most remain undiagnosed until rupture due to their asymptomatic nature. Improved prediction of aneurysm initiation could enable earlier detection and intervention. While computational hemodynamic models can identify high-risk regions, previous studies were limited to small cohorts due to labor-intensive manual workflows. We developed the first semi-automated workflow to enable large-scale, patient-specific hemodynamic analysis of IA initiation. METHODS: Our workflow integrates automated centerline extraction for quantitative morphological characterization with computational fluid dynamics (CFD) simulations to derive wall shear stress patterns and hemodynamic markers. We tested the workflow's robustness across multiple IA types and anatomical locations, focusing primarily on sidewall aneurysms of the internal carotid artery (ICA). RESULTS: Our semi-automated workflow successfully processed 42 diverse cases, 5 of them initially failed but were subsequently resolved through manual reconstruction, demonstrating robust performance across sidewall ICA aneurysms (16 cases), bifurcation aneurysms (6 cases), and validation cohorts. Validation against published data showed consistent trends with mean normalized TAWSS values of 1.31±0.09 in aneurysmal cases versus 1.14±0.07 in controls, aligning with previous findings despite methodological differences. CONCLUSIONS: The workflow's adaptability was confirmed across multiple anatomical configurations and region of interest selection methods. This scalable approach enables the statistical analysis necessary to identify reliable hemodynamic biomarkers for IA initiation, representing a critical advancement towards evidence-based prediction models for clinical risk stratification.