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iCAD announces FDA clearance of ProFound AI for digital breast tomosynthesis

Press releases may be edited for formatting or style | December 11, 2018 Women's Health
NASHUA, N.H., (December 7, 2018) – iCAD, Inc. (NASDAQ: ICAD), a global medical technology leader providing innovative cancer detection and therapy solutions, today announced clearance by the United States Food and Drug Administration (FDA) for their latest, deep-learning, cancer detection software solution for digital breast tomosynthesis (DBT), ProFound AI™, clearing the technology for commercial sales and clinical use in the United States. The powerful solution built on artificial intelligence (AI) is now available to healthcare facilities in the U.S., providing access to the most cutting-edge breast cancer detection software in the marketplace today.

“Obtaining FDA clearance for ProFound AI opens a new and substantial addressable market for iCAD. This enables us to offer clinicians globally an unrivaled cancer detection and workflow solution built on the latest advances in deep-learning,” said Stacey Stevens, Executive Vice President and Chief Strategy and Commercial Officer at iCAD. “Clinical reader study results and comprehensive stand-alone testing have shown unprecedented improvements in both clinical performance and reading efficiency. We are proud to introduce revolutionary technology that will fundamentally transform breast cancer detection and patient care.”

The FDA clearance is based on positive clinical results from a large reader study completed earlier this year and presented at this year’s Radiological Society of North America (RSNA) annual meeting at McCormick Place in Chicago. The research was performed with 24 radiologists who read 260 tomosynthesis cases both with and without iCAD’s ProFound AI solution. The findings show impressive results including increased cancer detection rates, reduced false positive rates and patient recalls, and a significant decrease in interpretation times.

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“This technology shows tremendous promise in assisting radiologists in detecting cancers, reducing recalls and increasing efficiency when reading tomosynthesis studies,” said Emily Conant, MD, Professor and Chief, Division of Breast Imaging, Vice Chair of Faculty Development, Department of Radiology at the Hospital of the University of Pennsylvania. “Clinical data shows that when tomosynthesis readers use the ProFound AI algorithm, case-level sensitivity is improved by 8 percent on average and reading times are significantly decreased. Radiologists with various levels of expertise may benefit from this AI-driven technology when reading large tomosynthesis data sets.”

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