QAS.AI
AI guides brain aneurysm surgery
Quantitative Angiographic Systems. Artificial Intelligence (QAS.AI) is developing an AI-driven algorithm that provides instant feedback to support a surgeon’s decision-making during delicate brain surgery. Its innovative approach aims at improving the accuracy of treatment outcomes for intracranial aneurysms (IAs) to optimize patient recovery rates and reduce potential risks. QAS.AI licensed technology developed at the University at Buffalo and has partnered with UB to continue advancing this life-saving intervention.
How QAS.AI collaborates with UB
Accelerates R&D
Licenses Technology
Recruits Talent
Collaborates with Experts
Occupies Office Space
Obtains Funding
Operates Tax Free
Accesses Legal Services
Receives Mentorship
CHALLENGE & OPPORTUNITY
Ruptured vessels cause deadly brain bleeds
A brain aneurysm, a balloon-like bulge in blood vessel, can rupture and cause hemorrhagic strokes responsible for nearly 500,000 deaths worldwide every year. Despite advances in medical treatments, around 65% of patients with ruptured brain aneurysms die from the initial bleed or subsequent complications. However, if a patient can get to a hospital in time, surgeons can attempt to repair the damaged blood vessel before it becomes a stroke. QAS.AI has developed an AI-enabled system that pulls outcome data from successful cases to guide surgeons’ decisions and significantly improve the odds of survival and recovery.
SOLUTION & OUTCOME
Software provides surgeon critical info in real time
QAS.AI is developing AI-informed software that can detect complications during surgery, such as inadequate blood flow in the brain, and forecast optimal treatment options—all in real time. The system reads images of the patient’s brain while on the operating table and advises the surgeon’s plan based on the outcome of similar past surgeries. The software leverages a vast database of brain scans and records collected from surgical centers around the country representing variations in populations, imaging machines, procedure protocols and clinician training to optimize predictions and outcomes. The platform has been integrated and validated at Gates Vascular Institute and works with Canon and Siemens imaging systems. QAS.AI is preparing for FDA de novo submission, with clinical trials launching in 2026.
UB SUPPORT
Tech transfer, funding and startup support improve performance
QAS.AI was founded by Ciprian Ionita, PhD, UB assistant professor of biomedical engineering and neurosurgery, who contacted UB’s Technology Transfer Office to facilitate intellectual property protection and commercialization. BEP supported the startup with mentorship, legal resources and space at UB’s Incubator @ CBLS. BEP funding through the Innovation Accelerator Fund and the Center for Advanced Technology in Big Data and Health Sciences (UB CAT) supports QAS.AI’s continued product development to grow its data set, fine-tune algorithms and improve the user interface. Early BEP support paved the way for significant federal funding to stage clinical evaluations that will prepare the company for the clinical trials needed to secure FDA approval and partnerships with imaging manufacturers.
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