PT  - JOURNAL ARTICLE
AU  - Lucas Elijovich
AU  - David Dornbos III
AU  - Christopher Nickele
AU  - Andrei Alexandrov
AU  - Violiza Inoa-Acosta
AU  - Adam S Arthur
AU  - Daniel Hoit
TI  - Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care
AID  - 10.1136/neurintsurg-2021-017714
DP  - 2022 Jul 01
TA  - Journal of NeuroInterventional Surgery
PG  - 704--708
VI  - 14
IP  - 7
4099  - http://jnis.bmj.com/content/14/7/704.short
4100  - http://jnis.bmj.com/content/14/7/704.full
SO  - J NeuroIntervent Surg2022 Jul 01; 14
AB  - 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.All data relevant to the study are included in the article or uploaded as supplementary information.