Partnership advances better neurovascular patient care by delivering timely insights to clinicians and improving clinical workflows

SAN FRANCISCO: #ASPECTS--Viz.ai, the leader in AI-powered disease detection and intelligent care coordination, and Avicenna.AI, a leading provider of AI-based medical imaging solutions, today announced that Viz.ai is integrating Avicenna.AI's CINA-iPE and CINA-ASPECTS tools into the Viz.ai OneTM platform. The combination of Avicenna.AI’s two AI-based algorithms with Viz.ai’s care coordination capabilities provides radiologists and specialists with additional insights from computed tomography (CT) scans, to advance fast and accurate decision-making, driving better patient care.

"This expanded partnership with Avicenna.AI represents an important step forward in our mission to improve patient care through AI-powered, clinical workflows," said Justin Ryea, director of product at Viz.ai. "By integrating CINA-iPE and CINA-ASPECTS into Viz.ai One, we are providing clinicians with an even more robust, all-in-one neurovascular solution that they can trust. This can help speed up the decision-making process and ensure that patients receive appropriate treatment as quickly as possible.”

The CINA-ASPECTS tool automatically assesses the ASPECTS (Alberta Stroke Program Early CT Score) from CT brain images, providing an objective and reliable measure of stroke severity. Avicenna.AI received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its CINA-iPE and CINA-ASPECTS tools in March 2024. The CINA-iPE tool detects incidental pulmonary embolism (PE), a life-threatening blockage in a lung artery, during routine CT scans and is intended to address delayed and missed findings. Unsuspected PE is a common finding in routine CT scans of the chest, but as little as 25% of emboli are reported during the initial interpretation.1 By combining Avicenna.AI’s technology with Viz.ai One, which facilitates real-time communication and coordination among care teams, clinicians can make faster, more informed decisions about treatment options for patients in need.

"We are thrilled to further our partnership with Viz.ai," said Cyril Di Grandi, CEO of Avicenna.AI. "Incorporating CINA-iPE and CINA-ASPECTS into Viz.ai’s platform will allow us to bring a higher level of quality and efficiency to patient care. Our shared goal of empowering healthcare professionals with innovative tools to make critical decisions quickly and confidently can improve outcomes for more patients by helping to diagnose life-threatening conditions faster and facilitating appropriate follow-up care to patients when they need it."

About Viz.ai

Viz.ai is the pioneer in the use of AI algorithms and machine learning to increase the speed of diagnosis and care across 1,700 hospitals and health systems in the U.S. and Europe. The AI-powered Viz.ai OneTM 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.

About Avicenna.AI

Founded in 2018, Avicenna.AI specializes in providing healthcare AI solutions that utilize deep learning to identify, detect, and quantify severe pathologies from CT medical images. Co-founded by Cyril Di Grandi, a successful entrepreneur who previously co-founded Olea Medical, and Dr. Peter Chang, an internationally recognized radiologist, and an expert in AI and deep learning, Avicenna.AI aims to accelerate therapeutic decision-making processes and enhance patient outcomes through its AI-based radiology solutions. For additional details, stay connected with us on social media and explore Avicenna.AI’s website at www.avicenna.ai.

1 Gladish GW, Choe DH, Marom EM, Sabloff BS, Broemeling LD, Munden RF. Incidental pulmonary emboli in oncology patients: prevalence, CT evaluation, and natural history. Radiology. 2006 Jul;240(1):246-55. doi: 10.1148/radiol.2401051129. Epub 2006 May 9. PMID: 16684921.

Fonte: Business Wire