ArticleAJNR. American journal of neuroradiology2021
Machine Learning-Based Prediction of Small Intracranial Aneurysm Rupture Status Using CTA-Derived Hemodynamics: A Multicenter Study.
Article in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants.Neurosurgical review · 2024Pooled it
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Pooled it
- Toward Patient-Specific Hemodynamic Decision-Making in Cerebral Aneurysms: The Maturation of Computational Fluid Dynamics and Fluid-Structure Interaction.Neurointervention · 2026Article
- Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.The international journal of cardiovascular imaging · 2026Article
- Multicenter-derived machine learning model for individualized rupture-risk prediction in intracranial aneurysms: study protocol and preliminary validation.Quantitative imaging in medicine and surgery · 2026Article
- A Machine learning pipeline to investigate tissue ingrowth in cerebral aneurysms using preclinical animal models.Scientific reports · 2026Article
- Morphological risk factors of parent arteries in intracranial aneurysm formation: an interpretable machine learning analysis.Neuroradiology · 2026Article
- Artificial Intelligence in Cerebrovascular Imaging: A Targeted Review of Aneurysm Detection and Rupture Risk Prediction.JMA journal · 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
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model.World journal of methodology · 2025Article
- Prediction of Recurrence and Rupture Risk of Ruptured and Unruptured Intracranial Aneurysms of the Posterior Circulation: A Machine Learning-Based Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Investigating the role of blood models in predicting rupture status of intracranial aneurysms.Biomedical physics & engineering express · 2025Article
- Machine Learning-Based Classification of Anterior Circulation Cerebral Infarction Using Computational Fluid Dynamics and CT Perfusion Metrics.Brain sciences · 2025Article
- Proposal of a Machine Learning Based Prognostic Score for Ruptured Microsurgically Treated Anterior Communicating Artery Aneurysms.Journal of clinical medicine · 2025Article
- Artificial intelligence applied to development of predictive stability model for intracranial aneurysms.European journal of medical research · 2024Article
- Article
- 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
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Authors and funding
11 authors.
Funding
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Abstract
background and purposeSmall intracranial aneurysms are being increasingly detected while the rupture risk is not well-understood. We aimed to develop rupture-risk models of small aneurysms by combining clinical, morphologic, and hemodynamic information based on machine learning techniques and to test the models in external validation datasets. MATERIALS AND
methodsFrom January 2010 to December 2016, five hundred four consecutive patients with only small aneurysms (<5 mm) detected by CTA and invasive cerebral angiography (or surgery) were retrospectively enrolled and randomly split into training (81%) and internal validation (19%) sets to derive and validate the proposed machine learning models (support vector machine, random forest, logistic regression, and multilayer perceptron). Hemodynamic parameters were obtained using computational fluid dynamics simulation. External validation was performed in other hospitals to test the models.
resultsThe support vector machine performed the best with areas under the curve of 0.88 (95% CI, 0.85-0.92) and 0.91 (95% CI, 0.74-0.98) in the training and internal validation datasets, respectively. Feature ranks suggested hemodynamic parameters, including stable flow pattern, concentrated inflow streams, and a small (<50%) flow-impingement zone, and the oscillatory shear index coefficient of variation, were the best predictors of aneurysm rupture. The support vector machine showed an area under the curve of 0.82 (95% CI, 0.69-0.94) in the external validation dataset, and no significant difference was found for the areas under the curve between internal and external validation datasets (
conclusionsThis study revealed that machine learning had a good performance in predicting the rupture status of small aneurysms in both internal and external datasets. Aneurysm hemodynamic parameters were regarded as the most important predictors.
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