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Densitas builds breast imaging AI the way clinical teams need it: rigorously validated, vendor-neutral, and engineered to deliver impact from acquisition to interpretation to program operations.
Models are developed and tested on multi-site, multi-vendor datasets and externally validated. Generalizability is designed in, with robustness checks across FFDM and DBT modalities, positioning variability, and diverse patient populations. Post-deployment performance tracking, and continuous quality improvement safeguard stability over time.
Clinical intelligence delivers consistent, reproducible assessments of breast composition and density aligned with established clinical categories. Risk assessment integrates density with clinical factors to support individualized screening strategies and supplemental tests.
Decision support provides human-in-the-loop review to reinforce safe, transparent clinical decisions. On the acquisition side, on-demand QA/QC delivers automated feedback on mammography positioning criteria and compression to reduce repeats/recalls and elevate technologist consistency, while equipment metrics are tracked to detect anomalies early and standardize mammography positioning across mammography units, and sites.
Operational intelligence optimizes mammography quality for capacity and throughput, streamlines recall workflows, and surfaces analytics – including unmet mammography positioning criteria rates and technologist variability – with drill-downs by site, mammography unit. Operational intelligence also drives proactive patient outreach to identify high-risk individuals and ensures targeted follow-up and adherence across subpopulations, and enables high risk clinic performance tracking.
Providers can realize fewer avoidable repeats and unnecessary callbacks, tighter and more consistent performance across technologists and mammography units, reproducible density and risk outputs that support personalized screening pathways, and better resource utilization.
Evidence-driven R&D with verifiable, reproducible results; end-to-end coverage from image acquisition to program management; vendor-neutral interoperability that respects your existing stack; and operational fluency that turns data into day-to-day decisions.
Scientifically valid and clinic-ready from day one, the Densitas intelliMammo platform combines rigorous science with real-world usability – standardizing density, elevating quality, and managing breast cancer risk to power efficient, accessible, and quality-assured care pathways – setting the standard so cancer doesn’t get missed
Laurie R. Margolies, Georgia G. Spear, Jennifer I. Payne, Sian E. Iles, Mohamed Abdolell
Published. Journal of Breast Imaging, May 30, 2025. doi:10.1093/jbi/wbaf025
N. Houssami, D. Lockie, M. Giles, N. Noguchi, G. Marr, L. Marinovich.
Published. British Journal of Radiology, 25 May 2023. doi:10.1259/bjr.20230081
N. Sharma, H. Heathcote-Watson, A. Nielsen Moody, M. Fletcher.
Poster presentation. Canadian Association of Radiologists (CAR) Annual Scientific Meeting. April 2022. Montreal, QC. Poster #66.
A.R. Brentnall, R. Warren, E.F. Harkness, S.M. Astley, J. Wiseman, J. Fox, L. Fox, M. Eriksson, J. Cuzick, D.G. Evans, A. Howell.
Breast Cancer Research (2020) 22:101. doi: 10.1186/s13058-020-01340-4
I. Lorne, E. Harkness, M. Harvie, P. Foden, A.Maxwell, DG. Evans, A. Howell, S. Astley.
Presentation. The British Society of Breast Radiology Annual Scientific Meeting. November 2017. Dublin, Ireland.
Astley, S., Harkness, E., Sergeant, J., Warwick, J., Stavrinos, P., Warren, R., Wilson, M., Beetles, U., Gadde, S., Lim, Y., Jain, A., Bundred, S., Barr, N., Reece, V., Brentnall, A., Cuzick, J., Howell, T. and Evans, D.
Breast Cancer Research, 20(1). doi: 10.1186/s13058-018-0932-z
G. Spear, L.R. Margolies, J. Payne, S. Iles, J. Seely, N. Sharma, S. Heywang-Köbrunner, T. Vomweg, M. Abdolell.
Presentation. 2025 European Congress of Radiology (ECR). February 26-March 2, 2025. Vienna, Austria.