Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients.
Journal:
Cerebrovascular diseases (Basel, Switzerland)
PMID:
33849032
Abstract
BACKGROUND AND PURPOSE: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes.
Authors
Keywords
Aged
Aged, 80 and over
Artificial Intelligence
Cerebral Angiography
Clinical Decision-Making
Computed Tomography Angiography
Databases, Factual
Decision Support Techniques
Delivery of Health Care, Integrated
Diagnosis, Computer-Assisted
Endovascular Procedures
Female
Humans
Ischemic Stroke
Male
Middle Aged
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Retrospective Studies
Time-to-Treatment
Triage
Workflow