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RadNet and Whiterabbit.ai Form a Comprehensive Partnership to Address Mammography Compliance and Improved Diagnostics for Breast Cancer
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WRDensity assists radiologists in interpreting breast density to create uniformity of density assessments at a practice level. The goal, aid in early detection¹ while giving patients peace of mind by improving consistency, confidence, and quality of care.
WRDensity was trained using deep learning based on over 600,000 images from Mallinckrodt Institute of Radiology, one of the world’s leading radiology research institutes.
Dr. Susan Holley
Clinical Director
Onsite Women's Health
Dr. Susan Holley
Clinical Director
Onsite Women's Health
WRDensity’s powerful deep learning model delivers highly accurate breast density classification.³
Our batching and sorting feature delivers up to a 20% increase in productivity for users of the product.⁴
WRDensity is presented within the radiologist’s existing workstation making relevant tissue density information available in context.
Find out how our products can help your business increase compliance, increase revenue and improve outcomes.
1 Mandelson MT, Oestreicher N, Porter PL, et al. Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst 2000;92(13):1081–1087
2 Engmann NJ, Golmakani MK, Miglioretti DL, Sprague BL, Kerlikowske K, for the Breast Cancer Surveillance Consortium. Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer. JAMA Oncol. 2017;3(9):1228–1236.
3 Whiterabbit data on file.
4 Whiterabbit data on file.
