Performance of an artificial intelligence tool for multi-step acute stroke imaging: A multicenter diagnostic study
This study demonstrates the reliable, stepwise performance of an AI-based stroke imaging tool across the diagnostic cascade of ICH and LVO detection and ASPECTS scoring. Such robust multi-stage evaluation supports its potential for streamlining acute stroke triage and decision-making.
Authors
- Angela Ayobi (ORCID: https://orcid.org/0000-0002-4351-6782)
- Nadine Girard (ORCID: https://orcid.org/0000-0001-8639-9275)
- Sarah Quenet
- Ahmed-Ali El Ahmadi (ORCID: https://orcid.org/0000-0002-4209-2113)
- Jean‐François Hak (ORCID: https://orcid.org/0000-0002-0225-7876)
- Yasmina Chaibi (ORCID: https://orcid.org/0000-0002-9734-172X)
- Gilles Brun (ORCID: https://orcid.org/0000-0001-7076-1954)
- Anthony Reyre
- Thibault Agripnidis (ORCID: https://orcid.org/0000-0003-2772-2954)
- Alexis Jacquier (ORCID: https://orcid.org/0000-0003-0297-7942)
- Christophe Avare
Institutions
- Centre National de la Recherche Scientifique (FR)
- Aix-Marseille Université (FR)
- Centre de Recherche et d’Enseignement de Géosciences de l’Environnement (FR)
- Hôpital Nord (FR)
- Institut Technique de l'Aviculture (FR)
- Institut de Neurosciences de la Timone (FR)
- Hôpital de la Timone (FR)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2025-08-29
- DOI
- https://doi.org/10.1016/j.ejro.2025.100678
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00