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.

Background/Objectives: Microsurgical clipping has traditionally been considered a standard treatment for middle cerebral artery (MCA) aneurysms. Recently, a caseload reduction related to improved endovascular treatment options has occurred in cerebrovascular neurosurgery. Therefore, studies that report the clinical and radiological outcomes after clipping are highly warranted. Methods: Patients with an unruptured MCA bifurcation aneurysm, who were surgically treated at the Department of Neurosurgery in Linz between 2002 and 2019, were included in this study. Clinical and radiological outcome parameters were evaluated for each patient. Results: Overall, 272 patients were eligible for inclusion. Complete aneurysm occlusion was demonstrated in 266 (99.3%) of the 268 (98.5%) patients who underwent postoperative digital subtraction angiography. In six (2.2%) patients, a permanent new neurological deficit (pNND) persisted after treatment. Intraoperative aneurysm rupture was a significant factor (p = 0.0049) in the logistic regression. At the last follow-up, only two patients (0.7%) had an unfavorable outcome (mRS > 2). More recent surgeries were associated with fewer cases of pNND (p = 0.009). A transient new neurological deficit occurred in 13 patients (4.8%), with aneurysm size being a significant risk factor (p = 0.009). Surgical site infections were reported in four patients (1.5%), with patient age (p = 0.039) and time (p = 0.001) being significant factors. Two patients died (0.7%) perioperatively and two patients (0.7%) needed a retreatment in the long-term follow-up. Conclusions: The findings indicate that microsurgical clipping is a safe procedure with minimal need for retreatment. It achieves a high occlusion rate while maintaining a very low rate of adverse outcomes. Continuous intraoperative enhancements over time have contributed to a progressive improvement in clinical outcomes in recent years. This trend is exemplified by the absence of detectable pNND in the era of ICG angiography. Consequently, these data support the conclusion that microsurgical clipping should still be considered an appropriate treatment option for unruptured MCA bifurcation aneurysms.

Introduction: The Anterior Communicating Artery complex (AComA) is one of the most common intracranial aneurysms locations. Accurate rupture risk assessment in patients with cerebral aneurysms is essential for optimizing treatment decisions. Computational fluid dynamics has significantly advanced insight into aneurysmal hemodynamics. Many studies concentrate predominantly on blood flow patterns, frequently neglecting the biomechanical properties of the aneurysm wall. Fluid-structure interaction analysis combines hemodynamic behavior with wall mechanics, potentially facilitating a more thorough evaluation of rupture risk assessment. Methods: In this study, we employed advanced techniques to investigate several single and composite parameters to predict the rupture risk of AComA aneurysms in a cohort of 150 patients treated at the Kepler University Hospital in Linz, Austria. For this reason, clinical, morphological, hemodynamic, and structural mechanical parameters were assessed. Results: A subsequent workflow analysis, consisting of comparative analysis, collinearity analysis, predictive modeling, composite parameter, performance evaluation, and internal threshold validation, revealed the Gaussian curvature GLN (AUC = 0.91) with a sensitivity of 0.93 and specificity of 0.83 as a best-performing single parameter for aneurysm rupture prediction. Composite parameters like WGD (combination of wall shear stress, Gaussian curvature, and wall displacement) achieved an AUC of 0.89, and WG (combination of wall shear stress and Gaussian curvature) an AUC of 0.88. An internal validation with 25 independent ruptured aneurysms was performed, and the previous results were confirmed with very high sensitivity values of 0.92 for GLN. Conclusion: Our findings indicate that the investigated morphological, hemodynamic, and structural, mechanical parameters could provide a potential tool for evaluating rupture risk for AComA aneurysms. The single morphological parameter GLN offers, followed by composite parameters WGD and WG, excellent prediction power for the aneurysm rupture state, as confirmed by internal validation. Further studies are warranted to evaluate the prospective clinical application of these parameters.

Background/Objectives: While biomechanically superior, cervical pedicle screw placement is technically challenging, and therefore typically performed only in centers with the aid of navigation. The purpose of this study was to analyze the accuracy and safety of navigated cervical pedicle screw (CPS) placement using intraoperative imaging with a workflow using a navigated high-speed drill in a large single-center cohort. Methods: We conducted a retrospective analysis of 205 patients undergoing posterior cervical or cervicothoracic instrumentation between January 2018 and June 2024. Accuracy was assessed using the Gertzbein–Robbins classification, with grades 0 and 1 considered satisfactory. Surgical workflow, intraoperative imaging, and complications were analyzed. Results: A total of 1112 pedicle screws, including 888 cervical and 224 upper thoracic screws, were evaluated. 801 were grade 0 (72.0%), 250 grade 1 (22.5%), 56 grade 2 (5.0%), and 5 grade 3 (0.4%). Cervical screws achieved satisfactory placement grades 0 and 1 in 93.1%, and upper thoracic screws in 100% (92.0% grade 0, 8.0% grade 1). Grade 3 breaches occurred in C2, C3, C5, C6, and C7, with one case each. There were no cases of implant-related neurovascular injuries. Conclusions: This study demonstrates high screw accuracy with a low observed complication rate. No revision surgeries were required due to screw malposition, but 7 cases of screw loosening occurred. However, the retrospective design and reliance on intraoperative imaging limit the generalizability of the findings.

Background/Objectives: Cervical pedicle screw (CPS) placement provides superior biomechanical stability but remains technically demanding and associated with a risk of screw malposition. While recent advances in imaging and navigation have improved placement accuracy, reliable prediction of malposition remains challenging. The aim of this study was to evaluate whether machine learning (ML) models can predict CPS malposition using structured clinical and anatomical features. Methods: We performed a retrospective analysis of 862 pedicle screws from 168 posterior cervical spine surgeries conducted at our institution between 2018 and 2025. Clinical, procedural, and anatomical variables, including age, sex, body size parameters, surgical indication, vertebral level, pedicle angle, and pedicle width, were evaluated. Pedicle morphology was partially derived from CT-based automated segmentation using TotalSegmentator (v2.13.0), while selected anatomical parameters were manually measured. Supervised ML models, including Random Forest, Balanced Random Forest, XGBoost (v3.2.0), Support Vector Machine, and K-Nearest Neighbor, were trained and compared using Python and scikit-learn to predict inaccurate screw placement. Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (ROC AUC), F1-score, precision, and recall. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results: The dataset showed a clinically representative class distribution, with 91.1% of screws classified as acceptable and 8.9% as inaccurate. Across all models, predictive performance was moderate and consistent. Balanced Random Forest achieved the highest discriminative performance (ROC AUC 0.69) and provided the most balanced classification profile, while other models demonstrated comparable overall performance with varying sensitivity to the minority class. SHAP analysis identified anatomical and procedural variables, including pedicle width and angle, as relevant contributors to model output. Feature contributions were distributed across variables, with substantial overlap between outcome groups. Conclusions: ML-based prediction of CPS malposition using clinical and anatomical features demonstrates consistent and interpretable performance. The results highlight that predictive performance is primarily influenced by dataset characteristics, including class distribution and feature overlap, rather than model selection alone. This study provides an important baseline for ML-based CPS prediction and supports future research integrating larger datasets and more detailed anatomical representations to enhance predictive accuracy.

Background/Objectives: Unruptured aneurysms of the anterior communicating artery (AComA) are associated with a higher risk of rupture and present unique anatomical challenges. Although endovascular techniques have advanced considerably, microsurgical clipping continues to represent an essential treatment option, particularly for complex cases. We conducted a retrospective analysis to evaluate outcomes of microsurgical clipping for unruptured AComA aneurysms over an 18-year period at a high-volume tertiary neurosurgical center. Methods: A retrospective analysis was conducted on 106 patients who underwent microsurgical clipping for unruptured AComA aneurysms between 2002 and 2020. Preoperative, intraoperative, and postoperative parameters were assessed. Excluded were previously ruptured or previously surgically treated aneurysms. Logistic regression models were used to identify predictors of postoperative complications with a focus on aneurysm projection. Results: Complete angiographic occlusion was achieved in 92.2% of cases, with a retreatment rate of 0.9%, which is comparable to the recent literature. Permanent neurological deficits occurred in 5.7% of patients. Posterior aneurysm projection was significantly associated with postoperative infarction and permanent neurological deficits. Intraoperative ICG angiography was associated with a reduced risk of ischemic complications. Conclusions: Microsurgical clipping remains a safe and effective treatment for unruptured AComA aneurysms in selected patients, offering durable occlusion and low complication rates. Aneurysm projection is a key predictor of outcome, and intraoperative ICG angiography significantly enhances surgical safety. These findings support the continued role of microsurgery in the interdisciplinary management of AComA aneurysms.