NASHUA, N.H. – April 19, 2021 - iCAD, Inc. (NASDAQ: ICAD), a global medical technology leader providing innovative cancer detection and therapy solutions, today announced that new research supporting the clinical value of ProFound AI® for Digital Breast Tomosynthesis (DBT) was presented at the Society of Breast Imaging (SBI) Symposium, April 9-11, and at the National Consortium of Breast Centers (NCBC) Annual Interdisciplinary Breast Center Conference (NCoBC), April 16-19.

Emily Conant, MD, Professor and Division Chief of Breast Imaging at the University of Pennsylvania Medical Center, presented findings from a retrospective analysis involving ProFound AI for DBT in a presentation titled "Feasibility of automated identification of low-likelihood of cancer cases in digital breast tomosynthesis screening," at the SBI Symposium. At the NCoBC Interdisciplinary Breast Center Conference, Mark Traill, MD, University of Michigan Health, presented findings from a study titled "Correlation between BI-RADS Assessment Categories and Artificial Intelligence Case Scores," which was a winner in the "Breast Disease Diagnosis and Management" category.

"These two studies both suggest that ProFound AI Case Scores provide valuable insights that can help clinicians more efficiently identify normal mammograms, which may directly translate to time-savings benefits," said Michael Klein, Chairman and CEO of iCAD. "In addition, ProFound AI is clinically proven to improve radiologists' sensitivity while simultaneously improving their specificity, which is a huge performance achievement in breast care. With the addition of these two important abstracts, research now shows how Case Scores can be used in the clinical setting to help radiologists feel more confident in their decisions about when a mammogram is normal."

According to study findings presented by Dr. Conant at the SBI Symposium, ProFound AI for DBT accurately identified 33.4 percent of normal screening DBT exams with no cancers being missed, based solely on the ProFound AI Case Score. When researchers also factored in breast density and age, ProFound AI identified 58.6 percent of normal cases with no false negatives.

"Our retrospective study demonstrates the feasibility that clinical algorithms have the potential to triage and reduce screening DBT workload by flagging normal mammograms using an AI system, and also prioritizing complex cases that are more likely to require additional review or evaluation," said Dr. Conant. "We are pleased to have our research add to the important growing body of evidence supporting the significance and value of AI in breast screening."

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