ArticleEuropean stroke journal2026
CATalyze-AI: accelerating referral for thrombectomy in acute stroke patients using an AI-based software. A prospective, multicentre, quasi-experimental cohort study.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
introductionTimely transfer of patients with suspected LVO remains critical in acute stroke systems. Artificial intelligence (AI)-based imaging tools are increasingly implemented to support triage in non-thrombectomy centres. We assessed whether integrating an AI algorithm within established tele-stroke centres reduces time to transfer decision. PATIENTS AND
methodsWe conducted a prospective, multicentre, quasi-experimental study comparing consecutive cohorts in 2 tele-stroke centres referring to a single comprehensive stroke centre, before and after implementation of the Methinks Stroke Suite, an AI algorithm for LVO detection on non-contrast CT (NCCT) and CTA. Consecutive transferred patients with suspected acute ischaemic stroke were included. Remote vascular neurologists retained responsibility for final transfer decisions. Each phase spanned approximately 15 months. The primary outcome was time from arrival at the local centre to emergency medical services activation. Secondary outcomes included workflow intervals, imaging utilisation and algorithm performance.
resultsWe included 265 patients (136 in the post-implementation cohort; 129 in the pre-implementation cohort). Adjusted median time from arrival to transfer request did not differ (median difference - 2.40 min; 95% CI, -6.16 to 4.48). Post-implementation, time from imaging to transfer request (-7.16 min; 95% CI, -13.02 to -1.85) and arrival to groin puncture (-31.28 min; 95% CI, -60.89 to -13.74) decreased. Computed tomography angiography acquisition at referring centres increased (28%-81%), reducing repeat imaging at the comprehensive centre (79%-42%). Non-contrast CT-based AI prediction yielded a positive predictive value of 66% for endovascular treatment.
conclusionArtificial intelligence implementation was not associated with a shorter time to transfer decision. Fewer redundant imaging examinations were associated with a shorter time to reperfusion.
Indexed as
Identifiers
What OpenQuestion holds
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.