ArticleJournal of clinical medicine2019
Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 5 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 5 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.Clinical neuroradiology · 2025Pooled 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
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled 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
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.Interdisciplinary sciences, computational life sciences · 2026Article
- Interpretable Machine Learning Identifies Key Inflammatory and Morphological Drivers of Intracranial Aneurysm Rupture Risk.Bioengineering (Basel, Switzerland) · 2026Article
- Diagnostic value of inflammatory indexes combined with myocardial enzymes for aneurysmal subarachnoid hemorrhage in patients with intracranial aneurysms.IBRO neuroscience reports · 2025Article
- Machine Learning-based Prediction of Temporal Velocity-Informatics (TVI) Variables for Accelerated Characterization of Intracranial Aneurysms' Rupture Status.Journal of cardiovascular translational research · 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
- Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques.Translational stroke research · 2025Review
- Using clinical and radiographic variables to predict intracranial aneurysm rupture status with machine learning.Surgical neurology international · 2025Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- Intracranial aneurysm CTA images and 3D models dataset with clinical morphological and hemodynamic data.Scientific data · 2024Article
- Artificial intelligence applied to development of predictive stability model for intracranial aneurysms.European journal of medical research · 2024Article
- Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.Bioengineering (Basel, Switzerland) · 2024Article
- Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
- Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model.Frontiers in physiology · 2024Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
The assessment of rupture probability is crucial to identifying at risk intracranial aneurysms (IA) in patients harboring multiple aneurysms. We aimed to develop a computer-assisted detection system for small-sized aneurysm ruptures using a convolutional neural network (CNN) based on images of three-dimensional digital subtraction angiography. A retrospective data set, including 368 patients, was used as a training cohort for the CNN using the TensorFlow platform. Aneurysm images in six directions were obtained from each patient and the region-of-interest in each image was extracted. The resulting CNN was prospectively tested in 272 patients and the sensitivity, specificity, overall accuracy, and receiver operating characteristics (ROC) were compared to a human evaluator. Our system showed a sensitivity of 78.76% (95% CI: 72.30%-84.30%), a specificity of 72.15% (95% CI: 60.93%-81.65%), and an overall diagnostic accuracy of 76.84% (95% CI: 71.36%-81.72%) in aneurysm rupture predictions. The area under the ROC (AUROC) in the CNN was 0.755 (95% CI: 0.699%-0.805%), better than that obtained from a human evaluator (AUROC: 0.537;
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.