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Densitas’ densityai deep learning algorithm is based on the latest BI-RADS Atlas breast composition classification guidelines and considers pixel intensity, texture and distribution. Density results have been rigorously validated for face-validity and have demonstrated almost perfect agreement with expert radiologist consensus.
Leveraging densityai supports the identification of higher-risk women who may benefit from supplemental screening with more sensitive imaging modalities.
Visual breast density assessments are highly variable – this is confusing and stressful for the patients. densityai provides reliable and reproducible breast density assessments in compliance with breast density reporting requirements.
Leveraging densityai breast density solutions supports the identification of high-risk women who may benefit from supplemental screening with more sensitive imaging modalities.
densityai uniquely uses a deep learning model that incorporates quantitative percent mammographic density with the qualitative 4-category (A, B, C, D) breast density scale that aligns with the ACR BIRADS Atlas 5th ed. breast density scale.
densityai specifically accounts for both the amount of mammographic density and the presence of localized areas of dense tissue, consistent with the ACR guidance to radiologists visually assessing breast density as rendered in mammograms.
densityai breast density assessments eliminate the subjectivity and lack of reliability associated with visual assessment, establishing standardization across entire health systems. Density results have been rigorously validated for face-validity and have demonstrated almost perfect agreement with expert radiologist consensus.
densityai uses routinely archived ‘for presentation’ images with results integrated into existing reporting and PACS workflows to boost efficiency and facilitate retrospective analyses.