Corrects and Replaces Announcement: Viz™ Neuro Suite’s Impact on Patient Care Validated by Six Clinical Studies

The second sentence of the first paragraph of the release should read: “Six studies, presented at the International Stroke Conference (ISC) 2024, have shown positive outcomes with real-world impact of in clinical practice across various neurology pathologies including acute ischemic stroke, cryptogenic stroke, unruptured incidental aneurysm (UIA), and brain hemorrhage.


Latest real-world evidence presented at the 2024 International Stroke Conference showcases the positive effects of AI-driven care coordination on clinical workflows and patient outcomes, a pioneering force in AI-powered disease detection and intelligent care coordination, has announced groundbreaking clinical findings that underscore advancements in neurovascular care. Six comprehensive studies, unveiled at the International Stroke Conference (ISC) 2024, demonstrate the tangible benefits of’s solutions in real-world clinical settings, spanning various neurology pathologies including acute ischemic stroke, cryptogenic stroke, unruptured incidental aneurysm (UIA), and brain hemorrhage.

Molly Madziva Taitt, VP of Global Clinical Affairs at, emphasized the company’s ongoing dedication to enhancing stroke detection and care coordination through its innovative Viz Neuro Suite. “At, we continuously strive to validate and improve the accuracy and impact of our comprehensive Viz Neuro Suite,” said Taitt. “Our unwavering commitment to prioritizing patients and facilitating early intervention underscores our core values.”

Abstracts featured at ISC include:

  • “Artificial Intelligence Algorithm as a Diagnostic Tool for Aneurysms”: This study evaluated the efficacy of the Viz Aneurysm algorithm, demonstrating its exceptional specificity (96.8%) and negative predictive value (97.2%) across 963 retrospectively-collected head CT scans. Dr. Justin Fraser of the University of Kentucky highlighted the significance of AI integration in identifying aneurysms swiftly and accurately.
  • “User Engagement is Associated with Clinical Impacts of Automated LVO Detection Software”: Post-hoc analysis of a multicenter, prospective, randomized clinical trial revealed an 11-minute reduction in door-to-groin (DTG) time across all centers with Viz LVO implementation. Dr. Sunil Sheth of UTHealth McGovern Medical School emphasized the pivotal role of end user engagement in enhancing acute stroke workflow efficiency.
  • “Estimation of Ventricular and Intracranial Hemorrhage Volumes and Midline Shift on an External Validation Data Set Using a Convolutional Neural Network Algorithm”: This study assessed the performance of Viz ICH+, a convolutional neural network (CNN), in automatically quantifying intracranial hemorrhage (ICH) and lateral ventricular (LV) volumes, as well as midline shift (MLS). The CNN demonstrated exceptional accuracy and reliability, offering significant time savings compared to manual neuroradiologist segmentation.
  • “Impact of Artificial Intelligence on Acute Ischemic Stroke Treatment in a Large Academic Healthcare System”: Findings from this study highlighted the considerable reduction in time-to-treatment and unnecessary transfers following the implementation of stroke triage artificial intelligence software in a large academic healthcare system.
  • “Door to Angiography in Large Vessel Occlusions Pre and Post Implementation of Automated Image Interpretation and Sharing Platform: A Single Center Study”: Results showcased shorter intervals between initial hospital contact and neurointervention among transferred patients following the implementation of Viz LVO software.
  • “Long-Term Cardiac Monitoring in Cryptogenic Stroke: The San Diego Experience”: This study evaluated the efficacy of long-term cardiac monitoring in detecting atrial fibrillation, particularly in cases of cryptogenic stroke, across two large academic Comprehensive Stroke Centers.

These groundbreaking studies affirm the substantial clinical benefits and enhanced patient care facilitated by’s AI-driven solutions in neurovascular care.

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