DenSeeMammo

DenSeeMammo is a software application designed to assist radiologists by providing an automated estimation of BI-RADS breast density categories from digital 2D mammograms. It achieves this by employing proprietary algorithms to analyze processed images and compare them against a qualified database of previously assessed mammograms, thereby offering adjunctive information to aid in breast density assessment.

DenSeeMammo version 1.2 is a software application designed to assist radiologists by estimating breast density from Full Field Digital Mammography (FFDM) images. It analyzes processed 2D digital mammograms using a fully automated comparison procedure to provide a BI-RADS 5th Edition breast density category. This adjunctive information is intended to aid in the assessment of breast density for both symptomatic and asymptomatic women undergoing mammography, but the final determination remains with an MQSA qualified interpreting physician. The software is compatible with DICOM images obtained from GE Senographe Essentials and Hologic Selenia Dimension systems and can process either single-view or two-view (craniocaudal and mediolateral oblique) mammograms. The software employs proprietary algorithms to compare patient images against qualified databases of mammograms previously assessed visually by radiologists according to ACR BI-RADS V recommendations, reporting results per patient based on the maximum density of the two breasts. Developed in Java and operating in a client-server architecture on a local network, DenSeeMammo's core is built for OS X, while its web-based graphical user interface is accessible on Windows, OS X, and Linux. Performance testing, including reproducibility studies, comparisons with expert radiologist assessments, and beta site clinical validation, demonstrated the software's ability to meet predefined acceptance criteria for accurate density estimation and system integration.

AI Regulatory Concerns

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• Q1. How does the performance of DenSeeMammo's automated BI-RADS categorization compare to inter-radiologist variability in breast density assessment, particularly across the different BI-RADS categories?; What specific image features or proprietary algorithms does DenSeeMammo utilize to differentiate between the BI-RADS density categories, and how were these algorithms validated against diverse patient populations and image qualities?; What is the quantitative impact on radiologists' workflow efficiency and assessment consistency when integrating DenSeeMammo as an adjunctive tool?; How does the software handle images with artifacts or unusual anatomical presentations, and what mechanisms are in place to flag cases where automated assessment might be less reliable?; What is the scalability of the "qualified databases" used for comparison, and how frequently are these databases updated or expanded to maintain accuracy with evolving imaging technologies and demographic shifts?