ArticleAJNR. American journal of neuroradiology2021
Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center.
Article in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it, 107 citations in OpenAlex.
- 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
- Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.Scientific reports · 2026Trial
- Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.Journal of neurointerventional surgery · 2026Trial
- Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: A Cluster Randomized Clinical Trial.JAMA neurology · 2023Trial
- Anatomy- and Time-Based Post-Processing with CT Perfusion-Derived Time-Vessel Maps to Reduce Incorrect Occlusion Detections in Late-Phase CT Angiography.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative generation of diffusion-weighted imaging from non-contrast CT in acute ischemic stroke: a multi-scanner deep-learning model with external validation.Quantitative imaging in medicine and surgery · 2026Article
- Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems.PLOS digital health · 2026Article
- Artificial intelligence (AI)-powered diagnostic support for stroke via Telegram bot: preliminary findings.BMC neurology · 2026Article
- Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings.Scientific reports · 2026Article
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Diagnostic Test Accuracy of Artificial Intelligence in Large Vessel Occlusion: A Systematic Review and Meta-Analysis.Neurology research international · 2026Review
- Performance of an artificial intelligence tool for multi-step acute stroke imaging: A multicenter diagnostic study.European journal of radiology open · 2025Article
- Evaluation of a Deep Learning Tool for Detecting Large Vessel Occlusion and Intracranial Hemorrhage on Noncontrast Computed Tomography Scans.Stroke (Hoboken, N.J.) · 2025Article
- Automated detection of large vessel occlusion using deep learning: a pivotal multicenter study and reader performance study.Journal of neurointerventional surgery · 2025Article
- Review
- Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications.Cureus · 2025Review
- Review
- Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm.Stroke (Hoboken, N.J.) · 2025Article
- Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.Clinical neuroradiology · 2025Article
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeArtificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel occlusions on CTA scans and notify the treatment team within minutes via a dedicated mobile application. We aimed to evaluate the detection accuracy of the Viz LVO in real clinical practice at a comprehensive stroke center. MATERIALS AND
methodsViz LVO was installed for this study in a comprehensive stroke center. All consecutive head and neck CTAs performed from January 2018 to March 2019 were scanned by the algorithm for detection of large-vessel occlusions. The system results were compared with the formal reports of senior neuroradiologists used as ground truth for the presence of a large-vessel occlusion.
resultsA total of 1167 CTAs were included in the study. Of these, 404 were stroke protocols. Seventy-five (6.4%) patients had a large-vessel occlusion as ground truth; 61 were detected by the system. Sensitivity was 0.81, negative predictive value was 0.99, and accuracy was 0.94. In the stroke protocol subgroup, 72 (17.8%) of 404 patients had a large-vessel occlusion, with 59 identified by the system, showing a sensitivity of 0.82, negative predictive value of 0.96, and accuracy of 0.89.
conclusionsOur experience evaluating Viz LVO shows that the system has the potential for early identification of patients with stroke with large-vessel occlusions, hopefully improving future management and stroke care.
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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.