ArticleRadiology. Artificial intelligence2020
Machine Learning Classification of Cerebral Aneurysm Rupture Status with Morphologic Variables and Hemodynamic Parameters.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.Clinical neuroradiology · 2025Pooled it
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Interpretable Machine Learning Identifies Key Inflammatory and Morphological Drivers of Intracranial Aneurysm Rupture Risk.Bioengineering (Basel, Switzerland) · 2026Article
- Interpretable machine learning model for predicting rupture risk in anterior communicating artery aneurysms.Neurosurgical review · 2026Review
- Interpretable model using serum biomarkers and intra-/peri-aneurysmal CTA radiomics for discriminating ruptured from unruptured intracranial aneurysms: an external validation retrospective study.Frontiers in neurology · 2026Article
- Radiomics and deep learning for intracranial aneurysm rupture-status assessment on CTA/MRA: a multicenter retrospective study.Frontiers in neuroscience · 2026Article
- Machine Learning-based Prediction of Temporal Velocity-Informatics (TVI) Variables for Accelerated Characterization of Intracranial Aneurysms' Rupture Status.Journal of cardiovascular translational research · 2025Article
- Coupling CFD and Machine Learning to Assess Flow Properties in Porous Scaffolds for Tissue Engineering.Micromachines · 2025Article
- Comparison of ELAPSS Score and Computational Fluid Dynamics for Predicting Growth of Small Unruptured Cerebral Aneurysms.Neurologia medico-chirurgica · 2025Observational
- Investigating the role of blood models in predicting rupture status of intracranial aneurysms.Biomedical physics & engineering express · 2025Article
- Improving Prediction of Intracranial Aneurysm Rupture Status Using Temporal Velocity-Informatics.Annals of biomedical engineering · 2025Article
- Computational fluid dynamics and shape analysis enhance aneurysm rupture risk stratification.International journal of computer assisted radiology and surgery · 2025Article
- Deep learning-based fine-grained assessment of aneurysm wall characteristics using 4D-CT angiography.PeerJ · 2025Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning.Scientific reports · 2024Article
- Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model.Heliyon · 2024Article
- Clinical management of a ruptured intracranial aneurysm.Frontiers in stroke · 2024Review
- Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.Frontiers in neurology · 2024Review
- Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo construct a classification model of rupture status and to clarify the importance of morphologic variables and hemodynamic parameters on rupture status by applying a machine learning (ML) algorithm to morphologic and hemodynamic data of cerebral aneurysms. MATERIALS AND
methodsBetween 2011 and 2019, 226 (112 ruptured and 114 unruptured) cerebral aneurysms in 188 consecutive patients were retrospectively analyzed with computational fluid dynamics (CFD). A random forest ML algorithm was applied to the results to create three classification models consisting of only morphologic variables (model 1), only hemodynamic parameters (model 2), and both morphologic variables and hemodynamic parameters (model 3). The accuracy of rupture status classification and the importance of each variable or parameter in the models were computed.
resultsThe accuracy was 77.0% in model 1, 71.2% in model 2, and 78.3% in model 3. The three most important features were projection ratio, size ratio, and aspect ratio in model 1; low shear area ratio, oscillatory shear index, and oscillatory velocity index in model 2; and projection ratio, irregular shape, and size ratio in model 3.
conclusionClassification models of rupture status of cerebral aneurysms were constructed by applying an ML algorithm to morphologic variables and hemodynamic parameters. The model worked with relatively high accuracy, in which projection ratio, irregular shape, and size ratio were important for the discrimination of ruptured aneurysms.
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