ArticleStroke (Hoboken, N.J.)2024
Machine Learning-Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers.
Article in Stroke (Hoboken, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Automated Emergent Large Vessel Occlusion Detection Using Viz.ai Software and Its Impact on Stroke Workflow Metrics and Patient Outcomes in Stroke Centers: A Systematic Review and Meta-analysis.Translational stroke research · 2025Pooled it
- Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.International journal of emergency medicine · 2026Review
- Implementation of an AI-supported decision-making tool in a high-volume stroke system with routine perfusion imaging.European stroke journal · 2026Article
- A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.Journal of personalized medicine · 2026Review
- Artificial Intelligence for Computer-Aided Detection in Endovascular Interventions: Clinical Applications, Validation, and Translational Perspectives.Bioengineering (Basel, Switzerland) · 2026Review
- Beyond Time Is Brain: The Time, Tissue, and Systems Framework for Acute Stroke.Journal of central nervous system disease · 2026Article
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Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Background: Accelerating door-in-door-out (DIDO) times at primary stroke centers (PSCs) for patients with large vessel occlusion (LVO) acute ischemic stroke transferred for possible endovascular stroke therapy (EVT) is important to optimize outcomes. Here, we assess whether automated LVO detection coupled with secure communication at non-EVT performing PSCs improves DIDO time and increases the proportion of patients receiving EVT after transfer. Methods: From our prospectively collected multicenter registry, we identified patients with LVO acute ischemic stroke that presented to one of 7 PSCs in the Greater Houston area from January 1, 2021, to February 27, 2022. Noncontrast computed tomography and computed tomographic angiography were performed in all patients at the time of presentation, per standard of care. A machine learning (artificial intelligence [AI]) algorithm trained to detect LVO (Viz.AI) from computed tomographic angiography was implemented at all 7 hospitals. The primary outcome of the study was DIDO at the PSCs and was determined using multivariable linear regression adjusted for sex and on/off hours. Secondary outcomes included likelihood of receiving EVT post-transfer. Results: Among 115 patients who met inclusion criteria, 80 were evaluated pre-AI and 35 post-AI. The most common occlusion locations were middle cerebral artery (51.3%) and internal carotid artery (25.2%). There were no substantial differences in demographics or presentation characteristics between the 2 groups. Median time from onset to PSC arrival was 117 minutes (interquartile range, 54-521 minutes). In univariable analysis, patients evaluated at the PSCs after AI implementation had a shorter DIDO time (median difference, 77 minutes; Conclusion: Implementation of a machine learning method for automated LVO detection coupled with secure communication resulted in a substantial decrease in DIDO time at non-EVT performing PSCs.
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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.