ArticleScientific reports2025
Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence for Intracranial Aneurysm Detection, Rupture-Risk Prediction, and Prognosis Evaluation: Recent Advances and Clinical Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Toward Patient-Specific Hemodynamic Decision-Making in Cerebral Aneurysms: The Maturation of Computational Fluid Dynamics and Fluid-Structure Interaction.Neurointervention · 2026Article
- A Ruptured Tri-Lobulated ICA-PCom Aneurysm Presenting with Preserved Neurological Function: Case Report and Clinical-Anatomical Analysis.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
13 authors.
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Abstract
Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 unruptured, 40 ruptured) using semi-automatic morphological analysis and high-resolution, image-based blood flow simulations from 3D rotational angiography. Advanced morphological and hemodynamic parameters were extracted, with clustering applied to address multicollinearity. Univariate logistic regression identified cluster representatives, and forward selection highlighted the maximum height, Neck inflow rate, and Non-sphericity index as rupture predictors, though only the latter two were significant. Clinical variables like age, sex, and comorbidities were also assessed but failed to predict rupture risk. The full model showed overfitting, with a pseudo-R
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