San Francisco, June 9, 2017 – Densitas Inc., developer of advanced breast imaging analytics technologies, announced that research is being presented today at University of California, San Francisco’s International Breast Density and Cancer Risk Assessment Workshop that demonstrates the unique value of Densitas’ automated breast density assessment software.

In the study, “Evaluation of Densitas, a mammographic density method for processed images,” researchers from the University of Manchester investigated the relationship between the software’s breast density assessments and cancer risk for interval and screen detected cancers. The study concludes Densitas’ automated breast density assessment algorithm produced density measures that predicted breast cancer risk and may be a practical method for risk-stratification of women using processed digital mammograms that are the default standard image format available for analysis at point-of-care.

“Our unique algorithm has the distinct advantage of permitting breast density assessments of routinely stored standard processed digital mammograms and their priors,” said Mohamed Abdolell, CEO of Densitas. “We’re pleased to see this study validating our technology. These key differentiators of our technology make our software the most clinically practical and pragmatic solution on the market.”

These research results uniquely position Densitas to provide a solution that overcomes the significant integration challenges associated with existing algorithms that only process RAW images. Densitas software integrates seamlessly with existing hospital and clinical IT infrastructures and does not alter routine clinical workflow, generating on-demand results at point-of-care.

About Densitas
Densitas Inc. is a medical software company building a comprehensive advanced imaging analytics platform that generates actionable information targeted at improving the mammography enterprise and associated clinical outcomes and appropriate use of health resources.

Alex Morris
Mobile: +1-647-470-4363

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