Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

Jacob R. Morey, Xiangnan Zhang, Kurt A. Yaeger, Emily Fiano, Naoum Fares Marayati, Christopher P. Kellner, Reade A. De Leacy, Amish Doshi, Stanley Tuhrim, Johanna T. Fifi

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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. Methods: A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. Results: The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; p = 0.01) with less variation (p < 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (p = 0.15). Conclusions: Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

Original languageEnglish (US)
Pages (from-to)450-455
Number of pages6
JournalCerebrovascular Diseases
Volume50
Issue number4
DOIs
StatePublished - Jul 1 2021

Keywords

  • Artificial intelligence
  • CT angiography
  • Stroke
  • Technology
  • Thrombectomy

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

Fingerprint

Dive into the research topics of 'Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients'. Together they form a unique fingerprint.

Cite this