Machine Learning–Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers

Machine Learning–Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers

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 co...

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Bibliographic Details
Main Authors: Ngoc Mai Le, Ananya S. Iyyangar, Youngran Kim, Mohammad Rauf Chaudhry, Sergio Salazar‐Marioni, Rania Abdelkhaleq, Arash Niktabe, Anjan N. Ballekere, Hussain Azeem, Sandi Shaw, Peri Smith, Mallory Cowan, Isabel Gonzales, Louise D. McCullough, Luca Giancardo, Sunil A. Sheth
Format: Article
Language:English
Published: Wiley 2024-05-01
Series:Stroke: Vascular and Interventional Neurology
Subjects:
artificial intelligence
CT angiogram
endovascular therapy
imaging
machine learning
stroke
Online Access:https://www.ahajournals.org/doi/10.1161/SVIN.123.001119

Internet

https://www.ahajournals.org/doi/10.1161/SVIN.123.001119

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