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Original research
Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care
  1. Lucas Elijovich1,2,
  2. David Dornbos III2,
  3. Christopher Nickele2,
  4. Andrei Alexandrov1,
  5. Violiza Inoa-Acosta1,2,
  6. Adam S Arthur2,
  7. Daniel Hoit2
  1. 1Department of Neurology, University of Tennessee Health Sciences Center, Memphis, TN, USA
  2. 2Department of Neurosurgery, Semmes-Murphey Clinic, University of Tennessee Health Sciences Center, Memphis, TN, USA
  1. Correspondence to Dr Lucas Elijovich, Semmes-Murphey Neurologic and Spine Institute, Memphis, TN 38120, USA; lelijovich{at}semmes-murphey.com

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.

  • CT angiography
  • stroke
  • technology
  • thrombectomy

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

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Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

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Footnotes

  • Twitter @DornbosIII_MD, @AdamArthurMD

  • Contributors All authors of this work met ICMJE criteria for authorship and made substantial contributions to the conception and design, acquisition of data, analysis and interpretation of data, drafting, critical revising, and final approval of this manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • 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.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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