Cerebral aneurysms present a persistent clinical paradox: although detected in roughly 3% of adults and only a minority rupture, aneurysmal subarachnoid hemorrhage still carries case-fatality rates approaching 30–35% and substantial longterm disability among survivors [1,2]. For decades, clinicians have determined whether to treat an aneurysm based mainly on its size, location, and shape. However, these factors alone remain insufficiently reliable. Some small aneurysms rupture unexpectedly, while many large aneurysms treated prophylactically may never have ruptured if left untreated [3]. Computational hemodynamics has long promised to narrow this gap, and advances over the past several years have brought the field substantially closer to clinically meaningful application.

Computational fluid dynamics (CFD) has matured from idealized, rigid-wall, steady-flow simulations into patient-specific pulsatile models reconstructed from rotational angiography or magnetic resonance angiography [4]. Wall shear stress (WSS), oscillatory shear index, and WSS gradient have become the dominant hemodynamic descriptors linked to aneurysm pathology [4,5]. Although debate persists regarding whether high or low WSS is more closely associated with rupture, a more nuanced consensus has emerged: both extremes appear capable of driving endothelial dysfunction through distinct mechanobiological pathways, and the local distribution of these stresses may be more important than their global average. To improve the limited specificity of single-parameter analyses, several groups have proposed composite indices integrating WSS with oscillatory shear and normalized hemodynamic metrics. These approaches have shown promising correlation with thin-walled aneurysm regions identified intraoperatively [6]. Collectively, these developments suggest that aneurysm rupture may ultimately depend less on flow alone than on the interaction between hemodynamic stress and intrinsic wall vulnerability.

The most consequential shift, however, is the move from CFD toward fluid-structure interaction (FSI). Rigid-wall CFD inherently neglects the compliant, often pathologically thinned aneurysm wall, where rupture ultimately occurs. Patient-specific FSI studies have shown that wall-strain based metrics can discriminate ruptured from unruptured aneurysms more reliably than WSS alone, while elevated strain appears to correlate more closely with aneurysm formation in certain anatomical locations [7,8]. Recent FSI analyses of middle cerebral artery aneurysms extended these findings: descriptors such as High Equivalent Stress Area and Gaussian curvature independently predicted rupture in cohorts of more than 100 patients, outperforming traditional morphological scores [9]. High-fidelity FSI has additionally begun to characterize aneurysm wall vibrations and flow-induced sound, opening a mechanobiological window into wall remodeling that rigid-wall models cannot access [10]. However, important limitations remain. Most contemporary FSI studies still rely on assumed wall thickness and constitutive material properties rather than true patient-specific measurements, limiting physiologic fidelity and cross-study reproducibility. In addition, the computational expense and technical expertise required for high-fidelity simulations continue to limit widespread clinical implementation.

Computational modeling is also beginning to reshape endo-vascular treatment planning. Virtual deployment of flow diverters and braided stents, coupled with CFD or FSI post-processing, permits estimation of intra-aneurysmal flow reduc tion, residual WSS, and the hemodynamic consequences of incomplete stent expansion before treatment [11]. Such simulations offer the possibility of individualized procedural optimization rather than empiric device selection alone.

For computational hemodynamics to meaningfully influence treatment selection, however, simulations must become sufficiently automated, rapid, and interpretable for integration into routine neurovascular workflows. Current methodologies remain highly dependent on operator expertise, segmentation technique, and solver configuration.

Machine learning is another great accelerator. In a 504-patient multicenter study, classifiers trained on computed tomography angiography-derived hemodynamic and morphological features achieved areas under the curve of 0.88–0.91 for rupture status of small aneurysms, with hemodynamic descriptors emerging as the most informative predictors [12]. Convolutional neural networks now permit rupture risk prediction directly from aneurysm geometry [13], and physics-constrained graph neural networks reproduce full-field aneurysmal hemodynamics in minutes rather than hours [14].

Importantly, however, many current machine-learning studies classify rupture status retrospectively rather than prospectively predicting future rupture risk. This distinction remains clinically critical. Negative findings in some cohorts further highlight the limited external validity of existing models and their tendency to generalize poorly outside the populations and imaging protocols on which they were trained [15]. External validation across institutions, imaging platforms, and patient populations therefore remains an essen tial unmet need

Three priorities should now guide the field. First, standardization remains urgently needed. Segmentation, meshing, solver settings, and source-imaging modality remain heterogeneous across centers, undermining cross-study com parability [4,5]. Second, validation must become more rigorous and systematic. Four-dimensional flow magnetic resonance imaging and patient-specific experimental phantoms should increasingly serve as benchmark references for computational studies. Without standardized in vivo validation, discrepancies between simulated and physiologic flow fields remain difficult to quantify. Third, integration into clinical decision-making must remain the ultimate objective. CFD and FSI outputs are clinically meaningful only when embedded within interpretable, prospectively validated tools that align with contemporary aneurysm management guidelines [2] and can guide bedside decision-making.

CFD told us where the blood goes; FSI is beginning to tell us when the wall will give way. Coupled with machine learning and rigorous validation, patient-specific computational hemodynamics is evolving from an experimental methodology into a potentially actionable instrument of personalized cere brovascular care. The challenge now is not whether these technologies can model aneurysm behavior, but whether they can do so robustly enough to meaningfully change clinical decisions. In addition, the future of aneurysm modeling may not lie in hemodynamics alone, but in coupling hemodynamic forces with biological markers of wall vulnerability, including inflammation, matrix degradation, and vessel wall imaging.