ArticleCerebrovascular diseases extra2022
Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support.
Article in Cerebrovascular diseases extra, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Impact of Artificial Intelligence-Based Triage on Stroke Workflow Metrics: A Systematic Review and Meta-Analysis.Cardiovascular and interventional radiology · 2026Pooled it
- Latest evidence on transfer efficiency and patient outcomes in mothership versus drip-and-ship models for acute stroke management: a systematic review and meta-analysis.Frontiers in neurology · 2026Pooled it
- AI in Neurological Health Care: Qualitative Study of Patient and Public Perceptions.Journal of medical Internet research · 2026Article
- Cerebral Infarction: Epidemiology, Classification, Mechanisms, Diagnosis, and Management.MedComm · 2026Review
- 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
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- A comprehensive guide to digital neurology. An update on current development on digital neurology derived from the Digital Neuro Hub project.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Evaluation of a Deep Learning Tool for Detecting Large Vessel Occlusion and Intracranial Hemorrhage on Noncontrast Computed Tomography Scans.Stroke (Hoboken, N.J.) · 2025Article
- Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications.Cureus · 2025Review
- Workload and clinical impact of MRI-based extension of reperfusion therapy time window in acute ischaemic stroke-a prospective single-centre study.GeroScience · 2025Article
- Prospective Evaluation of Artificial Intelligence Imaging Support Software for Acute Ischemic Stroke in the Mayo Clinic Telestroke Network.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- Exploring doctors' perspectives on generative-AI and diagnostic-decision-support systems.BMJ health & care informatics · 2025Article
- Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study.Journal of medical Internet research · 2025Article
- Use of artificial intelligence in the management of stroke: scoping review.Frontiers in radiology · 2025Review
- Current Stroke Solutions Using Artificial Intelligence: A Review of the Literature.Brain sciences · 2024Review
- Enhancing Efficiency with an AI-Augmented Clinician in Neurology.Aging and disease · 2024Article
- Achieving large-scale clinician adoption of AI-enabled decision support.BMJ health & care informatics · 2024Article
- Can AI Predict the Need for Surgery in Traumatic Brain Injury?Radiology. Artificial intelligence · 2024Article
- Adoption, orchestration, and deployment of artificial intelligence within the National Health Service-facilitators and barriers: an expert roundtable discussion.BJR artificial intelligence · 2024Review
- Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings.Yearbook of medical informatics · 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
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis is used for faster and standardized patient selection. However, there is little information on how such software influences real-world patient management.
aimsWe evaluated changes in thrombolysis and thrombectomy delivery following implementation of automated analysis at a high volume primary stroke centre.
methodsWe retrospectively collected data on consecutive stroke patients admitted to a large university stroke centre from two identical 7-month periods in 2017 and 2018 between which the e-Stroke Suite (Brainomix, Oxford, UK) was implemented to analyse non-contrast CT and CT angiography results. Delivery of stroke care was otherwise unchanged. Patients were transferred to a hub for thrombectomy. We collected the number of patients receiving intravenous thrombolysis and/or thrombectomy, the time to treatment; and outcome at 90 days for thrombectomy.
results399 patients from 2017 and 398 from 2018 were included in the study. From 2017 to 2018, thrombolysis rates increased from 11.5% to 18.1% with a similar trend for thrombectomy (2.8-4.8%). There was a trend towards shorter door-to-needle times (44-42 min) and CT-to-groin puncture times (174-145 min). There was a non-significant trend towards improved outcomes with thrombectomy. Qualitatively, physician feedback suggested that e-Stroke Suite increased decision-making confidence and improved patient flow.
conclusionsUse of artificial intelligence decision support in a hyperacute stroke pathway facilitates decision-making and can improve rate and time of reperfusion therapies in a hub-and-spoke system of care.
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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.