Evidence map›Paper›PMID 42623551›Full record

ArticleEuropean stroke journal2026

Implementation of an AI-supported decision-making tool in a high-volume stroke system with routine perfusion imaging.

Adrian Karlsson, Erik Cadeborn, Malin Woock, Arne Allardt, Katarina Jood, Isabella Björkman-Burtscher, Alexandros Rentzos

Abstract read
In one paragraph

Article in European stroke journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Adrian KarlssonDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0001-5428-9995
Erik CadebornSection of Diagnostic and Interventional Neuroradiology, Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden.ORCID 0009-0002-1225-7359
Malin WoockDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0002-9291-3151
Arne AllardtDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0009-0000-9142-0737
Katarina JoodDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0001-8746-1771
Isabella Björkman-BurtscherDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0002-9023-3363
Alexandros RentzosDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0001-6610-7802

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEndovascular thrombectomy (EVT) is the standard of care for LVO stroke; however, rapid workflow is critical for a beneficial outcome. Artificial intelligence (AI)-supported imaging tools, such as Brainomix 360 Stroke, may improve the efficiency of acute stroke imaging and reduce treatment delays. We evaluated the effects of regional implementation of Brainomix 360 Stroke in a high-volume system with routine use of perfusion imaging. PATIENTS AND

methodsWe conducted a retrospective register-based cohort study of consecutive patients treated with EVT at Sahlgrenska University Hospital from June 2021 to May 2024. Patients outside the Region Västra Götaland prehospital area or aged under 18 years were excluded. The study period was divided into pre-implementation, learning and established periods following Brainomix 360 Stroke introduction. Primary organisational outcomes were time from non-contrast brain CT (NCCT) to CT perfusion map availability (CT-to-perfusion-map-availability) and from NCCT to groin puncture (CT-puncture). Clinical outcomes included early neurological improvement (≥4 points on the NIHSS or a score of 0-1 at 24 h) and favourable functional outcome (mRS scores 0-2 or return to pre-stroke mRS at 90 days).

resultsA total of 970 EVT patients were included. Adjusted median CT-puncture significantly decreased from 47 min pre-implementation to 36 and 35 min in the learning and established periods, respectively. Adjusted median CT-to-perfusion-map-availability significantly decreased from 7 min pre-implementation to 6 min in the established period. In subgroup analyses of primary stroke centres (PSCs), results were similar while no significant differences were found in subgroup analyses of the comprehensive stroke centre. No significant differences were observed in clinical outcomes. DISCUSSION AND

conclusionImplementation of an AI-supported imaging decision-making tool was associated with significant reductions in key workflow times, largely attributable to decreased times at PSCs in the region.

Indexed as

Artificial IntelligenceClinical Decision-MakingPerfusion ImagingStrokeAgedAged, 80 and overEndovascular ProceduresFemaleHumansMaleMiddle AgedRegistriesRetrospective StudiesThrombectomyTomography, X-Ray Computedartificial intelligenceendovascular treatmentstroke

Identifiers

PMID42623551
PMCPMC13492657

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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.