ReviewNeurological research and practice2019
Computer-aided imaging analysis in acute ischemic stroke - background and clinical applications.
Review in Neurological research and practice, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis 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.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- MRI for collateral assessment pre-thrombectomy and association with outcome: a systematic review and meta-analysis.Neuroradiology · 2023Pooled it
- Predictive analysis in acute ischemic stroke imaging.Annals of medicine and surgery (2012) · 2026Review
- Roadmapping stroke healthcare in Colombia: a comprehensive telestroke network evaluation.BMC medical informatics and decision making · 2026Article
- Cerebral ischemia detection using deep learning techniques.Health information science and systems · 2025Article
- Artificial intelligence assisted detection of large vessel occlusion on CT angiography in acute stroke patients: a multi-reader multi-case study.BJR artificial intelligence · 2025Article
- Extensive Multilabel Classification of Brain MRI Scans for Infarcts Using the Swin UNETR Architecture in Deep Learning Applications.Annals of rehabilitation medicine · 2024Article
- Machine learning applications in stroke medicine: advancements, challenges, and future prospectives.Neural regeneration research · 2024Review
- Amyloid-related imaging abnormalities (ARIA): radiological, biological and clinical characteristics.Brain : a journal of neurology · 2023Review
- Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting Functional Outcome Using 24-Hour Post-Treatment Characteristics: Application of Machine Learning Algorithms in the STRATIS Registry.Annals of neurology · 2023Article
- Role of 3D Printing in Post-op Rehabilitation of Palatal Bone Loss by Mucormycosis: A Survey.Cureus · 2022Article
- Article
- Quantification of amyloid PET for future clinical use: a state-of-the-art review.European journal of nuclear medicine and molecular imaging · 2022Review
- Machine learning based analysis of stroke lesions on mouse tissue sections.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022Article
- Stem cells and common biomaterials in dentistry: a review study.Journal of materials science. Materials in medicine · 2022Review
- Labeling Noncontrast Head CT Reports for Common Findings Using Natural Language Processing.AJNR. American journal of neuroradiology · 2022Article
- A look back at 3 years of neurological research and practice (NRP).Neurological research and practice · 2022Article
- Imaging in Acute Anterior Circulation Ischemic Stroke: Current and Future.Neurointervention · 2022Review
- Variability of acquisition phase of computed tomography angiography in acute ischemic stroke in a real-world scenario.European radiology · 2022Article
- An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images.Journal of healthcare engineering · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in the last decade. Automatic evaluation of acute ischemic stroke in medical imaging is one of the fields that witnessed a major development. Commercially available products so far aim to identify (and quantify) the ischemic core, the ischemic penumbra, the site of arterial occlusion and the collateral flow but they are not (yet) intended as standalone diagnostic tools. Their use can be complementary; they are intended to support physicians' interpretation of medical images and hence standardise selection of patients for acute treatment. This review provides an introduction into the field of computer-aided diagnosis and focuses on the automatic analysis of non-contrast-enhanced computed tomography, computed tomography angiography and perfusion imaging. Future studies are necessary that allow the evaluation and comparison of different imaging strategies and post-processing algorithms during the diagnosis process in patients with suspected acute ischemic stroke; which may further facilitate the standardisation of treatment and stroke management.
Indexed as
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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.