Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care
| Autor: | Lucas Elijovich, Christopher Nickele, Adam S Arthur, Daniel Hoit, David Dornbos, Andrei V Alexandrov, Violiza Inoa-Acosta |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
business.industry General Medicine System of care medicine.disease Brain Ischemia Workflow Mechanical thrombectomy Treatment Outcome Artificial Intelligence Usual care Angiography Spoke-hub distribution paradigm Humans Medicine Surgery Neurology (clinical) Artificial intelligence business Stroke Ischemic Stroke Retrospective Studies Thrombectomy Large vessel occlusion |
| Zdroj: | Journal of NeuroInterventional Surgery. 14:704-708 |
| ISSN: | 1759-8486 1759-8478 |
| Popis: | BackgroundEmergent large vessel occlusion (ELVO) acute ischemic stroke is a time-sensitive disease.ObjectiveTo describe our experience with artificial intelligence (AI) for automated ELVO detection and its impact on stroke workflow.MethodsWe conducted a retrospective chart review of code stroke cases in which VizAI was used for automated ELVO detection. Patients with ELVO identified by VizAI were compared with patients with ELVO identified by usual care. Details of treatment, CT angiography (CTA) interpretation by blinded neuroradiologists, and stroke workflow metrics were collected. Univariate statistical comparisons and linear regression analysis were performed to quantify time savings for stroke metrics.ResultsSix hundred and eighty consecutive code strokes were evaluated by AI; 104 patients were diagnosed with ELVO during the study period. Forty-five patients with ELVO were identified by AI and 59 by usual care. Sixty-nine mechanical thrombectomies were performed.Median time from CTA to team notification was shorter for AI ELVOs (7 vs 26 min; pConclusionsAI automated alerts can be incorporated into a comprehensive stroke center hub and spoke system of care. The use of AI to detect ELVO improves clinically meaningful stroke workflow metrics, resulting in faster treatment times for mechanical thrombectomy. |
| Databáze: | OpenAIRE |
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