FDA Radiology AI Devices

Genius AI Detection is an AI software designed to assist radiologists by automatically identifying potential abnormalities such as masses and calcifications in digital breast tomosynthesis exams. It highlights suspicious regions on breast images, provides confidence scores, and helps improve diagnostic accuracy and workflow efficiency for breast cancer screening and diagnosis.

WRDensity by Whiterabbit.ai is a software tool that uses deep learning to analyze digital mammography images and automatically assess breast tissue composition according to the ACR BI-RADS 5th Edition breast density categories. It helps radiologists by providing breast density categorization and probability scores, which can improve breast cancer risk assessment and patient management. The software works with various mammography systems and outputs results to PACS and RIS for clinician review.

HealthMammo is an AI-enabled software tool designed to analyze 2D full-field digital mammograms to identify and flag suspicious findings. It assists radiologists by prioritizing mammogram cases in the PACS/worklist, helping clinicians review critical cases faster and improve workflow efficiency without replacing diagnostic evaluation.

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.

Transpara is an AI-powered software designed to assist physicians in interpreting full-field digital mammography and digital breast tomosynthesis exams. It highlights suspicious regions that may indicate breast cancer, providing scores that indicate the likelihood of malignancy. This helps radiologists improve detection accuracy and workflow efficiency while supporting clinical decisions during breast cancer screening.

Densitas densityai is a software application that analyzes digital mammography and breast tomosynthesis images to automatically assess breast tissue density. It provides a Breast Density Grade according to the American College of Radiology's BI-RADS 5th Edition to assist physicians in evaluating breast composition. This adjunctive tool helps interpret breast density consistently and efficiently, enhancing clinical decision-making in breast cancer screening.

Transpara is an AI-powered software designed to assist radiologists in detecting and diagnosing breast cancer from screening mammograms. It highlights suspicious soft tissue lesions and calcifications and provides likelihood scores for cancer presence, helping clinicians make more informed decisions during mammogram interpretation.

ProFound AI Software V2.1 is an AI-based software designed to assist radiologists in detecting soft tissue densities and calcifications in 3D digital breast tomosynthesis images. It highlights suspicious breast lesions by marking areas in the images and assigns confidence scores to help physicians identify likely malignant regions, supporting earlier and more accurate breast cancer diagnosis.

Koios DS for Breast is a machine learning-based software that assists physicians in analyzing ultrasound images of breast lesions. It helps classify breast lesions into BI-RADS categories to support diagnostic accuracy, improve consistency between readers, and provide useful visual confidence indicators. The software processes selected regions of interest on ultrasound images and provides diagnostic support to clinicians.

cmTriage is a software tool that uses artificial intelligence to analyze 2D digital mammograms and flags suspicious cases to help radiologists prioritize which exams to review first. It integrates with the Picture Archiving and Communication System (PACS) worklist but does not alter images or provide diagnostic results. cmTriage aims to improve the efficiency of breast cancer screening workflows by identifying potentially abnormal cases for quicker attention.