FDA Radiology AI Devices

The 6440 MyLabX90 by Esaote is an advanced multifunctional ultrasound scanner that supports real-time imaging and analysis during diagnostic ultrasound procedures. It integrates AI features like automated endocardial border detection for cardiac function assessment and lesion contouring for breast exams, helping clinicians improve diagnostic accuracy and efficiency across multiple body regions.

Lunit INSIGHT DBT is an AI-powered software designed to assist physicians in detecting and characterizing suspicious lesions for breast cancer in digital breast tomosynthesis (DBT) images. It analyzes DBT scans to identify and highlight regions of soft tissue lesions and calcifications with scores indicating malignancy likelihood, thereby supporting better screening and diagnosis of breast cancer in women.

Genius AI Detection 2.0 with CC-MLO Correlation is an AI-powered software designed to assist radiologists by analyzing digital breast tomosynthesis images to detect and mark suspicious soft tissue densities and calcifications. It provides confidence scores for each detected lesion, helping clinicians interpret breast cancer screening exams more effectively, including correlation between different standard mammographic views for better diagnosis workflow.

Genius AI Detection 2.0 is a software device that uses deep learning AI models to analyze digital breast tomosynthesis (DBT) images. It identifies and marks suspicious soft tissue densities and calcifications, providing confidence scores to aid radiologists during breast cancer screening. The AI improves detection specificity and reduces false positives, supporting clinicians in interpreting 3D mammography exams efficiently and accurately.

CogNet QmTRIAGE is an AI-powered software tool designed to analyze 2D digital mammograms to prioritize patients with suspicious breast findings. It flags exams suggestive of cancer to help mammography interpreting physicians prioritize their workflow, aiding early detection without replacing full diagnostic evaluation.

Transpara 1.7.2 is an AI-based software tool that helps radiologists detect and assess suspicious regions in breast mammography and tomosynthesis exams. It highlights potential cancerous lesions, scores their likelihood of malignancy, and provides an overall exam score to assist in clinical decision making, enhancing the accuracy and efficiency of breast cancer screening.

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.

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.