Abstract

AI-powered stroke triage system performance in the wild

Author(s): David Golan

Background: In February of 2018 the Food and Drug Administration (FDA) cleared Viz ContaCT, known commercially as Viz LVO- the first clearance for artificial-intelligence based software that detects large vessel occlusion (LVO) stroke and directly alerts relevant specialists, for the purpose of triage. The potential benefit is to reduce time to notification and therefore treatment, with better outcomes for patients. Recent publications have demonstrated time savings, improved patient outcomes, and reduction in hospital length-of-stay following an implementation of Viz LVO. Clinical evidence to support FDA clearance is typically generated in controlled settings, so it is important to characterize the performance of such software in the real-world, in which the data is more heterogeneous and unpredictable, coming from a number of sites with varying equipment, protocols and personnel skill levels.. Methods: 2,544 patients from 139 hospitals analyzed using the commercially available Viz LVO were sequentially collected and annotated. The results were used to evaluate the sensitivity and specificity of the Viz LVO software, as well as the time-to-notification of an LVO alert to the stroke team. Results: Viz LVO demonstrated high sensitivity (96.3% [92.6%-98.8%]) and specificity (93.8% [92.8%, 94.7%]) and a median time-to-notification of 5 minutes and 45 seconds across all of the sites involved. Conclusions: Viz LVO demonstrates robust performance despite the heterogeneity of setting, equipment, and processes. This suggests that the benefit documented at specific sites or systems may generalize to other hospital systems.


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