Viz.ai Announces Six Clinical Studies that Further Validate Impact of Viz™ Neuro Suite on Patient Care

Real-world evidence presented at the 2024 International Stroke Conference shows positive impact on clinical workflow and patient benefits of AI-powered care coordination

SAN FRANCISCO: #ai--Viz.ai, the leader in AI-powered disease detection and intelligent care coordination, today announced new clinical data supporting advancements in neurovascular care. Six studies, presented at the International Stroke Conference (ISC) 2024, have shown positive outcomes with real-world impact of Viz.ai in clinical practice across various neurology pathologies including acute ischemic stroke, unruptured incidental aneurysm (UIA) and brain hemorrhage.

“As a leader in AI-powered stroke detection and care coordination, Viz.ai does not rest on what we have done, but instead continues to invest in proving the accuracy and impact of our comprehensive Viz Neuro Suite,” said Molly Madziva Taitt, VP, Global Clinical Affairs. “Our commitment to neurology care teams and the patients who benefit from early intervention is a core value at Viz.ai, when we say ‘patients first,’ we mean it.”

Abstracts accepted for presentation at ISC:

• “Artificial Intelligence Algorithm as a Diagnostic Tool for Aneurysms” evaluated the performance of the Viz Aneurysm algorithm. The study found that across 963 retrospectively-collected head computed tomography (CT) scans, Viz Aneurysm demonstrated a specificity of 96.8% and a negative predictive value of 97.2%.

“Our findings show that Viz Aneurysm is a highly specific tool that can help to identify aneurysms,” said Justin Fraser, MD, FAANS, FAHA, Vice-Chair and Director of Cerebrovascular and Endovascular Surgery in the Department of Neurological Surgery, University of Kentucky. “The integration of AI technology into our aneurysm workflow helps us to promptly evaluate and triage these potentially unstable patients.”

• “User Engagement is Associated with Clinical Impacts of Automated LVO Detection Software” was a post-hoc analysis of the multicenter, prospective, randomized clinical trial. The analysis found an 11-minute reduction in door to groin (DTG) time across all centers with Viz LVO implementation. This study is the first of its kind to assess the impact of AI in the acute stroke workflow based on user engagement.

“Accurate and early detection of stroke using Viz LVO significantly improved our door to groin time,” said Sunil Sheth, MD, Director of Vascular and Interventional neurology, Associate Professor of Neurology, UTHealth McGovern Medical School. “These findings provide confirmation of the importance of end user engagement and interaction when using ML-based acute ischemic stroke large vessel occlusion detection.”

• “Estimation of Ventricular and Intracranial Hemorrhage Volumes and Midline Shift on an External Validation Data Set Using a Convolutional Neural Network Algorithm” evaluated the performance of a convolutional neural network (CNN), Viz ICH+, to automatically quantify intracranial hemorrhage (ICH) and lateral ventricular (LV) volumes as well as midline shift (MLS) compared to the more time intensive, manual neuroradiologists segmentation. The described CNN performed exceptionally well at quantifying ICH, LV volumes, and MLS with satisfying agreement on trend analysis in an independent validation imaging set.
  /li>
• “Impact of Artificial Intelligence on Acute Ischemic Stroke Treatment in a Large Academic Healthcare System” evaluated the impact of the implementation of a stroke triage artificial intelligence software on a large academic healthcare system. In addition to reduction in time-to-treatment, results show a large reduction in unnecessary transfers (60 transfers) throughout the two-year study.
  

• “Door to Angiography in Large Vessel Occlusions Pre and Post Implementation of Automated Image Interpretation and Sharing Platform: A Single Center Study” showed that implementation of Viz LVO software was associated with shorter intervals between initial hospital contact and neurointervention among transferred patients.
  

• “Long-Term Cardiac Monitoring in Cryptogenic Stroke: The San Diego Experience” assessed the real-world reliability and experience of implantable cardiac monitors (ICM) in two large, academic Comprehensive Stroke Centers (CSC). The study showed that long-term cardiac monitoring has been shown to detect significant rates of atrial fibrillation (AF), especially in cryptogenic stroke.

For more information on the Viz Neuro Suite, visit https://www.viz.ai/neuro.

About Viz.ai, Inc.

Viz.ai is the pioneer in the use of AI algorithms and machine learning to increase the speed of diagnosis and care, covering more than 230 million lives across 1,500+ hospitals and health systems in the U.S. and Europe. The AI-powered Viz.ai® One is an intelligent care coordination solution that identifies more patients with a suspected disease, informs critical decisions at the point of care, and optimizes care pathways and helps improve outcomes. Backed by real-world clinical evidence, Viz.ai One delivers significant value to patients, providers, and pharmaceutical and medical device companies. For more information visit viz.ai.

Fonte: Business Wire