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

ABSTRACT:

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 coupled with secure communication at non-EVT performing PSCs improves DIDO time and increases the proportion of patients receiving EVT after transfer.

Methods

From our prospectively collected multicenter registry, we identified patients with LVO acute ischemic stroke that presented to one of 7 PSCs in the Greater Houston area from January 1, 2021, to February 27, 2022. Noncontrast computed tomography and computed tomographic angiography were performed in all patients at the time of

SUBMITTER: Le NM 

PROVIDER: S-EPMC12778495 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature