SAN FRANCISCO & TEL AVIV, Israel--(BUSINESS WIRE)--Viz.ai, Inc. the leading applied artificial intelligence healthcare company, is excited to highlight real-world data from Dr. Ameer Hassan’s presentation “Early Experience Utilizing Artificial Intelligence Shows Significant Reduction in Transfer Times and Length of Stay in a Hub and Spoke Model.” Data released on Thursday is expected to demonstrate the impact of the Viz Stroke Platform on door-in door-out (DIDO) and Length of Stay (LOS) with patient transfer and treatment times. It will be presented at the International Stroke Conference in Los Angeles on Thursday (Feb. 20th).
Viz.ai’s comprehensive solution for LVO stroke detection and triage was implemented at Dr. Hassan’s hospital network in February 2019, including the spoke hospitals, which send large vessel occlusion (LVO) stroke patients to his hospital for mechanical thrombectomy. Dr. Hassan will be presenting during the Moderated Poster Session, Hall H, TMP62/MP62 at 5:35 p.m. PST.
“Viz is a revolutionary comprehensive solution for my entire hub and spoke network, which enables stroke workflow to smoothly transition from its traditional serial processes into parallel processes that allow for a reduction in transfer times and the potential for improved outcomes,” said Dr. Hassan.
Dr. Hassan is head of the Neuroscience Department and an Associate Professor of Neurology and Radiology at the University of Texas, School of Health Sciences. He serves as the Director of Endovascular Surgical Neuroradiology and Clinical Neuroscience Research at the Valley Baptist Brain and Spine Network in the Valley Baptist Medical Center. He has participated in numerous stroke clinical trials including SWIFT PRIME, DAWN and STRATIS studies.
“Dr. Hassan’s data further validates the real-world impact of implementing Viz LVO to improve access to timely mechanical thrombectomy, the standard of care for large vessel occlusion strokes,” said Chris Mansi, CEO and Co-Founder of Viz.ai. “The results from Dr. Hassan’s research further highlights the improvements to care that can be enabled by synchronizing workflow. It's encouraging to see the impacts on time and length of stay as our mission is to improve clinical outcomes.”
Viz LVO utilizes deep learning, a revolutionary subset of artificial intelligence where algorithms learn how to identify patterns in data from millions of prior examples for automatic detection of a large vessel occlusion. The Viz.ai system connects to a hospital CT scanner and alerts the stroke specialist that a suspected LVO stroke has been identified by sending the radiological images directly to their smartphone. These prompt notifications allow stroke teams to quickly and efficiently triage patients to time-sensitive care.
“Medtronic partnered with Viz.ai recognizing the potential this technology has to significantly reduce the time it takes for patients suspected of LVO stroke to receive the care they need,” said Stacey Pugh, vice president and general manager of Medtronic’s Neurovascular business, which is part of the Restorative Therapies Group at Medtronic. “We are very encouraged to see real-world evidence that Viz.ai’s software, coupled with our network, can improve access to needed therapies.”
About Viz.ai
Viz.ai is the leader in applied artificial intelligence in healthcare. Viz.ai’s mission is to fundamentally improve how healthcare is delivered in the world, through intelligent software that promises to reduce time to treatment and improve access to care. Viz.ai’s flagship product, Viz LVO, leverages advanced deep learning to communicate time-sensitive information about suspected stroke patients straight to a specialist who can intervene and treat.
In February 2018, the U.S. Food and Drug Administration (FDA) granted a De Novo clearance for Viz LVO, the first-ever computer-aided triage and notification software. Viz.ai announced its second FDA clearance for Viz CTP through the 510(k) pathway, offering healthcare providers an important tool for automated cerebral perfusion image analysis.
Viz.ai is located in San Francisco and Tel Aviv.
