Use of proper orthogonal decomposition and machine learning for efficient blood flow prediction in cerebral saccular aneurysms.

Accurate assessment of intracranial aneurysm rupture risk, particularly in Middle Cerebral Artery (MCA) aneurysms, relies on a detailed understanding of patient-specific hemodynamic behavior. In this study, we present an integrated framework that combines Computational Fluid Dynamics (CFD) with Proper Orthogonal Decomposition (POD) and machine learning (ML) to efficiently model pulsatile blood flow using a Casson non-Newtonian fluid model, without incorporating fluid-structure interaction (FSI). Patient-specific vascular geometries were reconstructed from DICOM imaging data and simulated using ANSYS Fluent to capture key hemodynamic factors, including velocity components, pressure, wall shear stress (WSS), and oscillatory shear index (OSI). POD was applied to reduce the dimensionality of the CFD data while retaining the dominant energetic flow structures. Results showed that fewer than 10 POD modes were sufficient to capture over 99% of the energy for pressure and WSS, while OSI required significantly more modes due to its inherent complexity. Machine learning models were trained on the reduced-order features to predict hemodynamic fields across time snapshots. The hybrid POD-ML approach yielded reasonable predictions for pressure and WSS in both training and test sets, while OSI prediction accuracy decreased in the test region, indicating the need for more advanced modeling strategies. The proposed method significantly reduces computational cost while preserving critical hemodynamic information, making it well-suited for real-time or near-real-time clinical decision support. This work demonstrates the potential of combining data-driven techniques with CFD for efficient, non-invasive risk assessment and treatment planning in cerebral aneurysm management.