This is Viz Neuro™.

VALIDATE: Validation of Artificial Intelligence to Limit Delays in Acute Stroke Treatment and Endovascular Therapy

Hospital utilization of an AI-based care coordination platform was associated with a significant decrease of 39.5 minutes in the time to NIR contact, increase in patients taken for intervention, and lower DTN times for thrombolytics.

A comparison of two automated CTP algorithms for estimation of core infarct

There was not a statistically significant difference in calculated rCBF<30% volumes between Viz or Rapid, compared to the manually segmented volumes from the 24 hour diffusion MRI.

Kalagara R et al. Automated Detection of Intracerebral Hemorrhage Using Artificial Intelligence: Pilot Deployment of Viz ICH

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.

Automated detection and analysis of subdural hematomas using a machine learning algorithm

Viz SUBDURAL CNN performed exceptionally well at identifying and quantifying key features of SDHs in an independent validation imaging data set.

Validation of an automated machine learning algorithm for the detection and analysis of cerebral aneurysms

The described Viz.ai Aneurysm CNN performed well in identifying the presence or absence of IAs in an independent validation imaging set. Further studies are necessary to investigate the impact of the software on detection rates in a real-world setting.