Integrating Deep Learning and Hemodynamic Simulations for Intracranial Aneurysm Management

Introduction Intracranial aneurysms (IAs) pose a major clinical challenge due to their unpredictable natural history and potentially catastrophic rupture. Conventional diagnostic paradigms rely on imaging morphology and clinical scoring, yet these lack precision in predicting outcomes. Deep learning (DL) has transformed neuroradiology by enabling automated aneurysm detection and segmentation on angiographic imaging. Parallel advances in computational fluid dynamics (CFD) have provided insight into hemodynamic stressors driving aneurysm growth and rupture. Integration of DL with hemodynamic simulations offers a pathway from “pixel to prognosis,” combining morphological recognition with patient‐specific flow dynamics to guide management. Methods A narrative review of the literature was conducted, synthesizing studies from 2018 to 2024 on DL‐based IA detection, radiomics, and CFD hemodynamic modeling. Evidence from systematic reviews, multicenter trials, and pilot integrations of imaging‐based AI with hemodynamic metrics was extracted. Particular attention was given to diagnostic accuracy (sensitivity, specificity, AUC), rupture risk prediction (AUC values, sensitivity/specificity for classification of ruptured vs. unruptured aneurysms), and workflow feasibility. Comparative analysis was performed to highlight the added value of integrating imaging features with CFD‐derived indices such as wall shear stress, oscillatory shear index, and flow patterns. Results DL models consistently achieved high detection performance, with pooled sensitivity and specificity exceeding 90% (Gu et al., 2022; Din et al., 2023). Segmentation algorithms such as vessel‐attention U‐Net attained Dice coefficients of 0.71‐0.78, enabling accurate aneurysm morphology extraction for CFD inputs (You et al., 2024). Radiomics studies demonstrated that textural and shape descriptors outperform size alone in rupture discrimination, with AUCs ranging from 0.82 to 0.98 (Hu et al., 2023). Hemodynamic models identified low wall shear stress and high oscillatory shear as strong rupture predictors. When integrated with DL‐extracted morphologies, ML‐CFD hybrid models achieved superior prognostic accuracy, with meta‐analytic AUC ∼0.90 versus ∼0.75 for size‐only models. Our pooled analysis confirmed that rupture prediction sensitivity improved by ∼10% with DL+CFD integration compared to imaging‐only approaches. Despite these advances, challenges remain in standardizing segmentation pipelines, computational cost of CFD, and validating results in prospective multicenter cohorts. Conclusion Integration of deep learning with hemodynamic simulations represents a paradigm shift in IA management, moving from detection to personalized rupture risk stratification. DL ensures scalable, accurate morphological extraction, while CFD enriches prediction with mechanistic hemodynamic insights. Together, these tools offer improved prognostic accuracy and the potential for patient‐specific treatment planning. Future directions include real‐time ML‐CFD hybrid models, harmonized datasets, and explainable AI frameworks to ensure clinical adoption.