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Ischemic Stroke l: Introduction

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.

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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

Artificial intelligence improves transfer times and ischemic stroke workflow metrics.

Nicholas C Field¹, Pouya Entezami², Alan S Boulos¹

¹Department of Neurosurgery, Albany Medical College, Albany, New York, USA.

Interventional Neuroradiology : Journal of Peritherapeutic Neuroradiology, Surgical Procedures and Related Neurosciences

|October 17, 2023

Summary

Implementing AI-powered active notifications for large-vessel occlusion (LVO) significantly reduced mechanical thrombectomy (MT) times. This AI tool improved patient transfer and workflow for ischemic stroke treatment.

Keywords:

Artificial intelligence ischemic stroke mechanical thrombectomy

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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

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Published on: January 18, 2018

Area of Science:

Neurology
Medical Imaging
Health Informatics

Background:

Mechanical thrombectomy (MT) is critical for treating large-vessel occlusion (LVO) ischemic stroke.
Rapid treatment initiation improves patient outcomes.
Artificial intelligence (AI) aids in LVO identification and workflow optimization.

Purpose of the Study:

To evaluate the impact of an AI algorithm with active cell phone notifications on ischemic stroke workflow.
To compare this new system with previous passive software analysis and radiologist notification.
To assess improvements in patient transfer times from outlying hospitals.

Main Methods:

Retrospective review of a thrombectomy registry (January 2020 - March 2022).
Collected demographic, radiographic, and workflow metrics, including notification times.
Compared pre- and post-implementation of the Viz.ai smartphone application for LVO detection.

Main Results:

Door-to-puncture time decreased by 15 minutes (p=0.009) post-Viz.ai implementation.
Transfer time from outside hospitals using Viz.ai reduced by 37 minutes (p=0.04).
No significant change in transfer time observed in hospitals not using Viz.ai.

Conclusions:

Active notification via AI significantly shortens patient transfer times.
This AI-driven approach accelerates the initiation of mechanical thrombectomy.
The findings support the integration of AI for optimizing acute stroke care pathways.