ArticleJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism2021
Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.
Article in Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- Comparing deep learning stroke segmentation in NCCT, CTA, and CTP: Accuracy, domain transfer, and temporal sampling effect.Medical physics · 2026Article
- Article
- Diagnostically competitive performance of a physiology-informed generative multi-task network for contrast-free CT perfusion.Frontiers in human neuroscience · 2026Article
- Enhanced diagnostic value of sequential head and neck CTA and MR-DWI in acute ischemic stroke.Medicine · 2025Observational
- Cerebral ischemia detection using deep learning techniques.Health information science and systems · 2025Article
- High ischemic deficit in computed tomography perfusion is a risk factor for cerebral vascular ischemic events in Takayasu arteritis: a prospective observational study.BMC rheumatology · 2025Article
- Generalizable self-supervised learning for brain CTA in acute stroke.Computers in biology and medicine · 2025Article
- Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estimation.BJR open · 2024Article
- Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review.Journal of multidisciplinary healthcare · 2023Review
- Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury.CNS neuroscience & therapeutics · 2023Article
- Deep learning and high-resolution magnetic resonance vascular wall imaging: current challenges and future perspectives.Frontiers in neurologyReview
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) patients. Computed tomographic perfusion (CTP) can be used to evaluate the ischemic core and deficit, covering most of the territories of anterior circulation, but many community hospitals and primary stroke centers do not have the capability to perform CTP scan in emergency situation. This study aimed to identify AIS lesions from widely available non-contrast computed tomography (NCCT) and CT angiography (CTA) using deep learning. A total of 345AIS patients from our emergency department were included. A multi-scale 3D convolutional neural network (CNN) was used as the predictive model with inputs of NCCT, CTA, and CTA+ (8 s delay after CTA) images. An external cohort with 108 patients was included to further validate the generalization performance of the proposed model. Strong correlations with CTP-RAPID segmentations (
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