ArticleScientific reports2020
Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- 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
- Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.Journal of neurointerventional surgery · 2023Pooled it
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- HSF-Net: a hybrid-scale fusion network for intracranial aneurysm segmentation in CTA images.Medical & biological engineering & computing · 2026Article
- A Joint Geometric Topological Analysis Network (JGTA-Net) for Detecting and Segmenting Intracranial Aneurysms.IEEE transactions on bio-medical engineering · 2025Article
- Methodological Challenges in Deep Learning-Based Detection of Intracranial Aneurysms: A Scoping Review.Neurointervention · 2025Review
- Automatic diagnosis and measurement of intracranial aneurysms using deep learning in MRA raw images.Frontiers in neurology · 2025Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- Detection of Subarachnoid Hemorrhage Using CNN with Dynamic Factor and Wandering Strategy-Based Feature Selection.Diagnostics (Basel, Switzerland) · 2024Article
- 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
- Impact of an AI software on the diagnostic performance and reading time for the detection of cerebral aneurysms on time of flight MR-angiography.Neuroradiology · 2024Article
- Article
- A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography.Biomedicines · 2023Review
- Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.Scientific reports · 2023Article
- An attention residual u-net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms.Medical image analysis · 2023Article
- Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage.World neurosurgery · 2023Article
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
- Segmentation Method of Cerebral Aneurysms Based on Entropy Selection Strategy.Entropy (Basel, Switzerland) · 2022Article
- DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images.Visual computing for industry, biomedicine, and art · 2022Article
- OTO-Net: An Automated MRA Image Segmentation Network for Intracranial Aneurysms.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subsequent treatment to prevent rebleeding. However, aneurysm detection proves to be challenging and time-consuming. The purpose of this study was to develop and evaluate a deep learning model (DLM) to automatically detect and segment aneurysms in patients with aSAH on computed tomography angiography. In this retrospective single-center study, three different DLMs were trained on 68 patients with 79 aneurysms treated for aSAH (2016-2017) using five-fold-cross-validation. Their outputs were combined to a single DLM via ensemble-learning. The DLM was evaluated on an independent test set consisting of 185 patients with 215 aneurysms (2010-2015). Independent manual segmentations of aneurysms in a 3D voxel-wise manner by two readers (neurosurgeon, radiologist) provided the reference standard. For aneurysms > 30 mm
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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.