iCAD Presents Latest AI Solution for Digital Breast Tomosynthesis
By MedImaging International staff writers
Posted on 28 Feb 2019
Image: ProFound AI offers an average of 8% in cancer detection rates, and a reduction by an average of 7% in the rate of unnecessary patient recalls (Photo courtesy of iCAD).
iCAD Inc. (Nashua, NH, USA), a provider of cancer detection and therapy solutions, presented its latest artificial intelligence (AI) software solution for digital breast tomosynthesis (DBT), ProFound AI, during the European Congress of Radiology (ECR) 2019, the second-largest radiological meeting in the world. Held by the European Society of Radiology, the conference took place February 27 through March 3, 2019, in Vienna, Austria.
ProFound AI is a deep-learning, cancer detection and workflow solution for DBT that delivers critical benefits to radiologists, their facilities and patients through an improvement by an average of 8% in cancer detection rates and a reduction by an average of 7% in the rate of unnecessary patient recalls. The technology is designed to detect malignant soft-tissue densities and calcifications and provides radiologists with the certainty of finding lesion and case scores.
At ECR 2019, iCAD previewed ProFound AI for 2D mammography, which is built on deep learning technology and is currently pending CE mark. iCAD also led two clinical presentations during the conference and hosted an event entitled, “AI After Dark.”
“With growing global demand for cancer detection and workflow solutions built on the latest advances in deep-learning, we are thrilled to demonstrate the unrivaled capabilities of our ProFound AI solution, which is clinically proven to help clinicians detect more cancers, decrease false positives and reduce recalls and reading time,” said Stacey Stevens, Executive Vice President and Chief Strategy and Commercial Officer at iCAD. “ProFound AI offers tremendous benefits to radiologists and their patients in identifying cancer earlier. We look forward to expanding the unique capabilities of our algorithm through new application areas, including breast cancer risk prediction, in the future.”