ArticleFrontiers in neuroscience2022
Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Radiomics for differentiating ruptured intracranial aneurysms: overview, methodological quality evaluation using METRICS and RQS, and feature transportability validation using independent multi-center dataset.European radiology · 2026Pooled it
- The roles of radiomics and deep learning for automatic detection, stability assessment, and rupture risk prediction in intracranial aneurysms: a systematic review.European journal of medical research · 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
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Review
- Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model.Frontiers in physiology · 2024Article
- A simple and effective machine learning model for predicting the stability of intracranial aneurysms using CT angiography.Frontiers in neurology · 2024Article
- Clinical-Radiomics Nomogram Model Based on CT Angiography for Prediction of Intracranial Aneurysm Rupture: A Multicenter Study.Journal of multidisciplinary healthcare · 2024Article
- Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis.Frontiers in neurology · 2023Article
- EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres.Frontiers in neuroscience · 2022Article
- Development and validation of a nomogram for predicting early rupture and rebleeding risk after intracranial aneurysm embolization.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the
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