FDA Radiology AI Devices

Saige-Dx is an AI-based software that analyzes digital breast tomosynthesis mammograms to detect suspicious soft tissue lesions and calcifications that may indicate cancer. It provides radiologists with a suspicion level for each finding and the entire case, helping improve detection accuracy and reduce reading time during breast cancer screening.

BU-CAD is a software application designed to assist physicians by analyzing breast ultrasound images to identify and evaluate soft tissue lesions suspicious for breast cancer. It highlights regions of interest and provides malignancy scores and BI-RADS classifications to support clinical decision-making. It also supports viewing of mammography images and includes tools for image adjustment and documentation, helping improve diagnostic accuracy and efficiency.

Koios DS is a web-based AI software that helps trained physicians analyze ultrasound images of breast lesions and thyroid nodules suspicious for cancer. It automatically selects regions of interest, classifies lesions using standardized lexicons like BI-RADS and TI-RADS, provides cancer risk assessments, and generates structured reports to aid in clinical decision-making, improving diagnostic accuracy and reducing variability.

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.

Lunit INSIGHT MMG is an AI-powered computer-assisted detection and diagnosis software designed to help radiologists detect, localize, and characterize suspicious areas for breast cancer on mammograms. It works as an adjunct tool viewed after the initial physician read, providing visual marks and scores indicating the likelihood of malignancy to assist clinical decision-making and improve breast cancer detection during mammography screenings.

ClariSIGMAM is a software tool designed to automatically analyze digital mammograms to estimate breast density by calculating the ratio of fibroglandular tissue to the total breast area. It categorizes breast density using BI-RADS groups to assist radiologists in assessing breast tissue composition, providing adjunctive information to support interpretation but is not a diagnostic aid.

breastscape v1.0 is an advanced software tool used with Olea Sphere 3.0 to assist breast imaging physicians and MRI technologists in analyzing breast MRI data. It provides visualization, semi-automatic lesion segmentation, kinetic curve analysis, and planning assistance for MR-guided breast biopsy procedures, helping clinicians detect and characterize breast lesions and plan interventions more effectively.

Volpara Imaging Software is an AI-driven tool that processes raw digital mammography and tomosynthesis images to generate detailed breast density maps and quantitative metrics. It helps healthcare professionals assess breast composition by providing volumetric measurements and BI-RADS density categories as adjunctive information in breast cancer screening and assessment.

PowerLook Density Assessment V4.0 is a software tool that analyzes synthetic 2D images from digital breast tomosynthesis exams to provide breast density categories using the ACR BI-RADS 5th Edition. This helps radiologists assess breast tissue composition as an adjunctive aid, improving breast cancer screening processes.

Transpara 1.7.0 is an AI software tool designed to assist physicians in interpreting breast imaging exams such as digital mammography and tomosynthesis. It uses deep learning algorithms to detect suspicious calcifications and soft tissue lesions, scoring their likelihood of malignancy to aid diagnosis and improve workflow. It processes images to highlight abnormal areas and provides exam-level cancer likelihood scores, supporting better identification of breast cancer indicators.