FDA Clears iCAD’s ProFound AI for Digital Breast Tomosynthesis

iCAD Inc., a developer of cancer detection and therapy solutions, 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.
 
“This technology shows tremendous promise in assisting radiologists in detecting cancers, reducing recalls and increasing efficiency when reading tomosynthesis studies,” said Emily Conant, M.D., 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.”
 
ProFound AI is a high-performance, deep-learning, cancer detection and workflow solution for DBT delivering critical benefits to radiologists, their facilities, and their patients through improvement of cancer detection rates by an average of 8 percent and decreasing unnecessary patient recall rates by an average of 7 percent. The new technology is trained to detect malignant soft-tissue densities and calcifications. It also provides radiologists with scoring information representing the likelihood that a detection or case is malignant based on the large dataset of clinical images used to train the algorithm.
 
In addition to improving clinical performance related to breast cancer detection and false positive rates, study results showed that ProFound AI can reduce radiologists’ reading time by more than 50 percent on average. An increase in reading time has been a significant challenge for radiologists when moving from 2D to 3D mammography.
 
The solution is currently available for use with leading DBT systems in the U.S., Canada, and Europe.