ProFound AI® for Tomosynthesis 

ProFound AI for breast tomosynthesis is clinically proven to assist radiologists in addressing the challenges of reading tomosynthesis cases by:

  • Improving cancer detection rates (8% improvement in sensitivity)

  • Reducing false positives and unnecessary patient recalls (7% reduction in rate of recalls)

  • Decreasing reading times (52.7% reduction in reading time for radiologists) 

ProFound AI® for 2D MammographY

The high-performing cancer detection and workflow solution rapidly analyzes mammographic images, detecting both malignant soft tissue densities and calcifications with unrivaled accuracy.
The AI algorithm delivers superior performance for the detection of breast cancer and high specificity with low numbers of false positives. ProFound AI 2D alone can reach a sensitivity of 95%. In a study realized on 17910 screening mammograms, ProFound AI alone had a better breast cancer detection (with 95% sensitivity) than the two readers who participated in the study (84.6% and 89.7% sensitivity).

PowerLook Density Assessment

PowerLook® Density Assessment is an automated breast density solution that is designed to standardize the assessment of breast density in 2D and 3D mammography. It assists radiologists in evaluating and scoring breast density to identify patients who may need supplemental screening or be at higher risk of developing breast cancer. This solution uses an appearance-based approach to assess dense tissue, to deliver automated, rapid and reproducible assessments of breast structure, texture, and fibroglandular dispersion.

ProFound AI RisK

Revolutionizing Personalized Screening.
A new solution available on the ProFound AI platform, ProFound AI Risk, is the first and only commercially available solution that combines age, breast density, and mammographic images to provide an accurate estimate of a patient’s individual risk of developing breast cancer within two years. Research published in the peer-reviewed journal Radiology shows that ProFound AI Risk significantly outperforms existing breast cancer risk models, which may accelerate movement towards risk-adaptive screening.