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. 2022 Jul;14(7):704-708.
doi: 10.1136/neurintsurg-2021-017714. Epub 2021 Aug 20.

Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care

Lucas Elijovich  1   2 , David Dornbos Iii  2 , Christopher Nickele  2 , Andrei Alexandrov  3 , Violiza Inoa-Acosta  3   2 , Adam S Arthur  2 , Daniel Hoit  2
Affiliations

Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care

Lucas Elijovich et al. J Neurointerv Surg. 2022 Jul.

Abstract

Background: Emergent large vessel occlusion (ELVO) acute ischemic stroke is a time-sensitive disease.

Objective: To describe our experience with artificial intelligence (AI) for automated ELVO detection and its impact on stroke workflow.

Methods: We 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.

Results: Six 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; p<0.001). Door to arterial puncture was faster for transfer patients with ELVO detected by AI versus usual care transfer patients (141 vs 185 min; p=0.027). AI yielded a time savings of 22 min for team notification and a 23 min reduction in door to arterial puncture for transfer patients.

Conclusions: AI 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.

Keywords: CT angiography; stroke; technology; thrombectomy.

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Conflict of interest statement

Competing interests: LE serves as a consultant for Balt, Cerenovus, Medtronic, Micro Vention, Penumbra, and Stryker. DD has no competing interests. CN is a consultant for Leica and has received research support from Microvention. AA has no competing interests. VI-A reports no competing interests. ASA is a consultant for Johnson and Johnson, Medtronic, Microvention, Penumbra, Scientia, Siemens, and Stryker; receives research support from Balt, Cerenovus, Medtronic, Microvention, Penumbra, Siemens, and Stryker; and is a shareholder in Bendit, Cerebrotech, Endostream, Magneto, Marblehead, Neurogami, Serenity, Synchron, Triad Medical, Vascular Simulations. DH serves as a consultant for Covidien/Medtronic and Microvention; and is a shareholder of Cerebrotech, Marblehead Medical, and Silver Bullett.

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