ArticleStroke2019
Fast Automatic Detection of Large Vessel Occlusions on CT Angiography.
Article in Stroke, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- MR Imaging versus Noncontrast CT for Selecting Patients with Acute Ischemic Stroke of Large Vessel Occlusion for Endovascular Thrombectomy: A Systematic Review and Meta-Analysis.AJNR. American journal of neuroradiology · 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
- Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA.Neuroinformatics · 2026Article
- Swarm Learning for the Detection of Middle Cerebral Artery Occlusion On CT Angiography.Clinical neuroradiology · 2026Article
- Initial Experience With a Deep Learning Algorithm for Detecting Posterior Circulation Large Vessel Occlusion on Noncontrast Computed Tomography.Stroke (Hoboken, N.J.) · 2026Article
- RAPID CTA versus JLK LVO for large vessel occlusion detection: a pragmatic comparison of performance and common pitfalls.Neuroradiology · 2026Article
- Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai.Medicina (Kaunas, Lithuania) · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Automated detection of large vessel occlusion using deep learning: a pivotal multicenter study and reader performance study.Journal of neurointerventional surgery · 2025Article
- Review
- Diagnostic yield and therapeutic implications of vascular imaging in acute ischemic stroke: prospective and consecutive study of small vessel versus large vessel ischemia.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2025Article
- StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.NPJ digital medicine · 2024Article
- A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA.iScience · 2024Article
- Article
- Article
- Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT.Diagnostics (Basel, Switzerland) · 2023Article
- Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study.La Radiologia medica · 2023Article
- Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.Nature communications · 2023Article
- Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes.Frontiers in neurology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Background and Purpose- Accurate and rapid detection of anterior circulation large vessel occlusion (LVO) is of paramount importance in patients with acute stroke due to the potentially rapid infarction of at-risk tissue and the limited therapeutic window for endovascular clot retrieval. Hence, the optimal threshold of a new, fully automated software-based approach for LVO detection was determined, and its diagnostic performance evaluated in a large cohort study. Methods- For this retrospective study, data were pooled from: 2 stroke trials, DEFUSE 2 (n=62; 07/08-09/11) and DEFUSE 3 (n=213; 05/17-05/18); a cohort of endovascular clot retrieval candidates (n=82; August 2, 2014-August 30, 2015) and normals (n=111; June 6, 2017-January 28, 2019) from a single quaternary center; and code stroke patients (n=501; January 1, 2017-December 31, 2018) from a single regional hospital. All CTAs were assessed by the automated algorithm. Consensus reads by 2 neuroradiologists served as the reference standard. ROC analysis was used to assess diagnostic performance of the algorithm for detection of (1) anterior circulation LVOs involving the intracranial internal carotid artery or M1 segment middle cerebral artery (M1-MCA); (2) anterior circulation LVOs and proximal M2 segment MCA (M2-MCA) occlusions; and (3) individual segment occlusions. Results- CTAs from 926 patients (median age 70 years, interquartile range: 58-80; 422 females) were analyzed. Three hundred ninety-five patients had an anterior circulation LVO or M2-MCA occlusion (National Institutes of Health Stroke Scale 14 [median], interquartile range: 9-19). Sensitivity and specificity were 97% and 74%, respectively, for LVO detection, and 95% and 79%, respectively, when M2 occlusions were included. On analysis by occlusion site, sensitivities were 90% (M2-MCA), 97% (M1-MCA), and 97% (intracranial internal carotid artery) with corresponding area-under-the-ROC-curves of 0.874 (M2), 0.962 (M1), and 0.997 (intracranial internal carotid artery). Conclusions- Intracranial anterior circulation LVOs and proximal M2 occlusions can be rapidly and reliably detected by an automated detection tool, which may facilitate intra- and inter-instutional workflows and emergent imaging triage in the care of patients with stroke.
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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.