Data from several new studies presented at the recent International Stroke Conference (ISC 2022; 9–11 February, New Orleans, USA) further validate the “best-in-class” sensitivity and specificity of Viz.ai’s intelligent care coordination platform for stroke in real-world settings across multiple commercial modules—according to a press release from the company.
Across three posters and one abstract presented at ISC 2022 from three institutions, it was concluded that there is a positive impact to workflow and patient care from the use of multiple Viz platform modules, including Viz LVO (for large vessel occlusion) and Viz ICH (for intracranial haemorrhage).
Real-world data from the University of California at San Diego (UCSD; San Diego, USA) demonstrated reductions in door-to-groin (DTG) time of 41 minutes for direct arriving LVO patients in a robust comprehensive stroke centre. In addition, data from Ohio State University, Wexner (Columbus, USA) indicated the accuracy of Viz ICH in alerting 82 of 83 ICH cases detected by radiologists, according to Viz.ai.
Furthermore, a recent publication from Mount Sinai Hospital (New York, USA), titled “AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study,” by Stavros Matsoukas (Icahn School of Medicine at Mount Sinai, New York, USA) et al, saw data presented from a study of 1,822 computed tomography angiography (CTA) scans—demonstrating detection rates of 100% and 93% for internal carotid artery terminus (ICA-T) and M1 occlusions, respectively.
The authors concluded: “In this work, the diagnostic accuracy of Viz LVO was tested in real life and real time across one of the to-date largest prospective consecutive cohorts, in a multiple-tiered healthcare system. Viz LVO is a promising AI-driven software that can reliably detect ICA-T and M1 LVOs with impressive NPV [negative predictive value], sensitivity, and overall accuracy. It is a useful adjunct in triaging patients with an LVO stroke at varying levels of stroke centres.”
As per the Viz.ai release, an additional abstract from the same centre studied 682 patients analysed with Viz ICH and found an overall accuracy of 99%. “Viz ICH has the potential to be an adjunct tool to streamline ICH triage, reduce treatment delays, and improve outcomes of patients presenting with ICH,” the authors of this study concluded.
“These new real-world data further demonstrates the unparalleled accuracy of the Viz platform, which results in time savings and improved stroke patient outcomes,” said Jayme Strauss, chief clinical officer of Viz.ai.