ArticlePatterns (New York, N.Y.)2021
Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
What it found
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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
25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 61 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
- When combinations of humans and AI are useful: A systematic review and meta-analysis.Nature human behaviour · 2024Pooled it
- Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2023Pooled it
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- 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
- The TopCoW Challenge - Topology-Aware Circle of Willis Segmentation for CT and MR Angiography.NEJM AI · 2026Article
- Anatomically-guided masked autoencoder pre-training for aneurysm detection.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2026Article
- Article
- Automated, anatomy-based, heuristic post-processing reduces false positives and improves interpretability of deep learning intracranial aneurysm detection models.Scientific reports · 2025Article
- Article
- Worthwhile or Not? The Pain-Gain Ratio of Screening Routine cMRIs in a Maximum Care University Hospital for Incidental Intracranial Aneurysms Using Artificial Intelligence.Journal of clinical medicine · 2025Article
- Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques.Translational stroke research · 2025Review
- CTA image segmentation method for intracranial aneurysms based on MGLIA net.Scientific reports · 2025Article
- MTMU: Multi-domain Transformation based Mamba-UNet designed for unruptured intracranial aneurysm segmentation.BMC medical imaging · 2025Article
- Evaluation of AI-Powered Routine Screening of Clinically Acquired cMRIs for Incidental Intracranial Aneurysms.Diagnostics (Basel, Switzerland) · 2025Article
- Vessel-aware aneurysm detection using multi-scale deformable 3D attention.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.Frontiers in neurology · 2024Review
- Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan.Brain sciences · 2023Article
- A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography.Biomedicines · 2023Review
- A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
21 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracranial aneurysm (IA) is an enormous threat to human health, which often results in nontraumatic subarachnoid hemorrhage or dismal prognosis. Diagnosing IAs on commonly used computed tomographic angiography (CTA) examinations remains laborious and time consuming, leading to error-prone results in clinical practice, especially for small targets. In this study, we propose a fully automatic deep-learning model for IA segmentation that can be applied to CTA images. Our model, called Global Localization-based IA Network (GLIA-Net), can incorporate the global localization prior and generates the fine-grain three-dimensional segmentation. GLIA-Net is trained and evaluated on a big internal dataset (1,338 scans from six institutions) and two external datasets. Evaluations show that our model exhibits good tolerance to different settings and achieves superior performance to other models. A clinical experiment further demonstrates the clinical utility of our technique, which helps radiologists in the diagnosis of IAs.
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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.