ArticleJournal of medical imaging (Bellingham, Wash.)2019
Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke.Journal of medical systems · 2024Pooled it
- Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2022Pooled it
- Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings.Scientific reports · 2026Article
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs.JMIR neurotechnology · 2024Article
- A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke.Frontiers in neurology · 2024Review
- Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke.Frontiers in neuroscience · 2022Article
- Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning.Frontiers in neuroscience · 2022Article
- Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods.Life (Basel, Switzerland) · 2021Review
- Artificial Intelligence and Acute Stroke Imaging.AJNR. American journal of neuroradiology · 2021Review
- Artificial Intelligence in Neuroradiology: Current Status and Future Directions.AJNR. American journal of neuroradiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of
Indexed as
Identifiers
What OpenQuestion holds
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.