Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture

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-auto...

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Bibliographic Details
Main Authors: Vanessa M. Swiatek, Samuel Voss, Florian Sprenger, Igor Fischer, Hafez Kader, Klaus-Peter Stein, Roland Schwab, Sylvia Saalfeld, Ali Rashidi, Daniel Behme, Philipp Berg, I. Erol Sandalcioglu, Belal Neyazi
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Small intracranial aneurysms
Rupture risk assessment
Semiautomatic neck curve reconstruction
Computational fluid dynamics
Predictive modeling
Online Access:https://doi.org/10.1038/s41598-025-08478-1
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https://doi.org/10.1038/s41598-025-08478-1

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