Door to Puncture in Large Vessel Occlusions Pre‐ and Postimplementation of an Automated Image Interpretation and Communication Platform: A Single Center Study

Abstract

Background: Artificial intelligence platforms, like Viz.ai with large vessel occlusion detection, have been used for disease detection and interprovider communication. Whether this software expedites patient transfer and evaluation for treatment needs further exploration. Methods: A single-center retrospective registry was queried for patients with acute large vessel occlusion of the intracranial internal carotid, middle cerebral M1 or M2 segments, or basilar artery treated in a comprehensive stroke network (8 spokes, 1 hub) for 6 months pre- and post-implementation of the Viz large vessel occlusion platform (excluding a 1-month "washout" period). Robust regression was used to summarize time from initial hospital contact to arterial puncture (primary outcome) between periods, with prespecified subgroup analyses, which were assessed using interaction terms. Results: <0.01). Conclusion: Implementation of the artificial intelligence platform was not associated with shorter intervals between initial hospital contact and neurointervention among all-comers. A meaningful difference in time to treatment was observed among transferred patients. Larger data sets are needed to validate these observations.

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