Register Now: Image-Based Risk: The Next Frontier in Breast Cancer Screening
|Articles|August 1, 2021

Volpara Receives FDA Clearance for Breast Density Algorithm

Software improves on breast density assessment algorithm with artificial intelligence.

Volpara Health announced Friday it has received 510(k) clearance from the U.S. Food & Drug Administration for the latest version of Volpara Imaging Software.

This version – VIS 3.2 – incorporates learnings from artificial intelligence to augment the robustness of the breast density assessment algorithm. In addition, this clearance approves VIS 3.2 for use on other mammography machines, including those from Giotto and Siemens.

The new version offers improved imaging processing security through the Open Virtual Appliance architecture, and it also makes it easier for Volpara to monitor, services, and update software, company officials said.

“These new innovations improve the overall security, scalability, robustness, and breadth of our breast health offering,” said Ralph Highnam, Ph.D., Volpara’s Group chief executive officer. “Our objective with each enhancement is the pursuit of our mission – to eliminate advanced-stage breast cancer and save more families from cancer.”

With this approval, Volpara marks its fifth clearance since launching Volpara Imaging Software in 2010. It is a key science algorithm that supports the Volpara Breast Health Platform, a product suite designed to improve early breast cancer detection through augmented mammography quality and workflow, volumetric breast density assessment, and personalized breast care.

For more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter here.



Related to this article

Current Perspectives on Integrating AI into Radiology
In a recent interview with Diagnostic Imaging, Joseph Cavallo, MD, MBA, offered insights on post-deployment monitoring with AI, the potential for improved workflow efficiency and the ongoing challenge of balancing essential clinical skills and AI literacy in the training of radiology residents and fellows.