FDA Radiology AI Devices

MammoScreen® is an AI-based software designed to assist radiologists in detecting and diagnosing lesions that may be indicative of breast cancer from mammography images. It helps improve accuracy and efficiency by highlighting suspicious areas, supporting earlier and more reliable cancer detection.

MammoScreen BD is an AI-powered software that helps radiologists evaluate breast tissue density in mammograms and digital breast tomosynthesis. It provides breast density categories based on established ACR BI-RADS standards. The tool offers supplementary information to aid radiologists in assessing breast density, supporting better screening decisions for women aged 40 and older. It processes DICOM images and integrates with clinical systems to assist interpretation without replacing physician judgment.

MammoScreen 3 is an artificial intelligence software tool designed to assist physicians in interpreting screening mammograms from digital mammography and digital breast tomosynthesis systems. It provides graphical marks indicating suspicious soft-tissue lesions or calcifications, along with a suspicion score and lesion characterization, helping radiologists in their diagnosis and reporting. The software is intended as an aid to, but not a replacement for, the physician's clinical judgment.

MammoScreen 2.0 is an AI-based software tool designed to assist physicians by automatically analyzing standard screening mammograms including FFDM and DBT images. It marks suspicious soft tissue lesions or calcifications on breast images and provides a level of suspicion score to help radiologists improve detection and characterization of breast cancer findings. This tool acts as a concurrent reading aid to improve diagnostic accuracy but does not replace physician judgment.

MammoScreen is an AI-based software that assists physicians in interpreting full-field digital mammograms (FFDM) by identifying suspicious breast lesions such as soft tissue lesions and calcifications. It provides marks on the mammogram images alongside a suspicion score to help detect potential breast cancer, supporting radiologists during their reading process to improve cancer detection without replacing clinical judgment.