Background: Hemodynamics during the growth process of cerebral aneurysms are incompletely understood. We developed a novel fluid–structure interaction analysis method for the identification of relevant scenarios of aneurysm onset. Method: This method integrates both fluid dynamics and structural mechanics, as well as their mutual interaction, for a comprehensive analysis. Patients with a single unruptured cerebral aneurysm were included. Results: Overall, three scenarios were identified. In scenario A, wall shear stress (WSS) was low, and the oscillatory shear index (OSI) was high in large areas within the region of aneurysm onset (RAO). In scenario B, the quantities indicated a reversed behavior, where WSS was high and OSI was low. In the last scenario C, a behavior in-between was found, with scenarios A and B coexisting simultaneously in the RAO. Structural mechanics demonstrated a similar but independent trend. Further, we analyzed the change in hemodynamics between the onset and a fully developed aneurysm. While scenarios A and C remained unchanged during aneurysm growth, 47% of aneurysms in scenario B changed into scenario A and 20% into scenario C. Conclusions: In conclusion, these findings suggest that WSS and the OSI are reciprocally regulated, and both low and high WSS/OSI conditions can lead to aneurysm onset.

Background: Cerebral vasospasm is a frequent and severe complication after aneurysmal subarachnoid hemorrhage (aSAH), often causing delayed cerebral ischemia (DCI) and poor outcomes. Despite progress in neurocritical care, early vasospasm prediction after aSAH remains challenging due to its multifactorial nature but is essential for timely intervention. Methods: We retrospectively analyzed 503 consecutive patients with spontaneous subarachnoid hemorrhage (SAH) treated between 2013 and 2018. Of these, 345 with angiographically confirmed aSAH were included in the primary analysis, and 158 SAH cases in a sensitivity analysis. We extracted demographic, clinical, and imaging parameters including age, sex, Hunt and Hess grade, Fisher scale, aneurysm and treatment features, external ventricular drainage (EVD), and central nervous system (CNS) infection. Seven supervised machine learning (ML) models, including logistic regression and gradient-boosted trees, were trained using nested cross-validation and evaluated by AUC-ROC, AUC-PR, accuracy, precision, sensitivity, specificity, and F1 score. Results: Over half of aSAH patients developed moderate to severe vasospasm. Independent predictors included younger age, higher Hunt and Hess and Fisher grades, and EVD placement (all p < 0.001). Logistic regression achieved the best discrimination (AUC-ROC 0.723), while tree-based models reached higher sensitivity (0.867) at the expense of specificity. Aneurysmal etiology further increased vasospasm risk (OR 4.72). Conclusions: Routinely available clinical and imaging parameters enable reliable ML-based vasospasm prediction after aSAH. Logistic regression provided the best balance between accuracy and interpretability, while tree-based models optimized sensitivity. This web-based, interpretable ML tool—one of the first using routine clinical data—may support the bedside prediction of vasospasm and requires prospective validation.