FDA Radiology AI Devices

Versana Premier is a general purpose diagnostic ultrasound system designed for use by trained healthcare professionals. It supports multiple clinical applications such as fetal, abdominal, gynecological, cardiac, vascular, musculoskeletal, and more. It includes various advanced imaging modes and features AI-based enhancements like Whizz Label to assist clinicians in acquiring and interpreting ultrasound images efficiently and accurately. The system is intended for hospitals and medical clinics to aid in the diagnosis and assessment of medical conditions.

The Aquilion Exceed LB is a whole-body multi-slice helical CT scanner that captures detailed cross-sectional images of the body, including the head. It incorporates AiCE, an AI-based noise reduction algorithm using deep convolutional neural networks, to improve image quality and reduce noise. This helps clinicians obtain clearer, more accurate diagnostic images for various body regions, including abdomen, pelvis, lung, cardiac, extremities, head, and inner ear.

The Philips EPIQ and Affiniti Series Diagnostic Ultrasound Systems are advanced ultrasound imaging devices used to capture diagnostic ultrasound images and analyze fluid flow in various parts of the human body. These systems include an AI-powered Auto Measure feature that semi-automates 2D and Doppler measurements during echocardiography, helping clinicians perform more efficient and standardized assessments with editable, semi-automated results.

AmCAD-UT is a Windows-based AI-assisted software designed to help clinicians analyze thyroid ultrasound images. After initial review by a physician, the software quantifies and visualizes sonographic features of thyroid nodules, specifically those larger than 1cm, aiding in clinical decision-making for biopsy recommendations. It facilitates detailed image analysis and generates automated reports to support thyroid cancer diagnosis and management.

The V8 Diagnostic Ultrasound System by Samsung Medison is a general-purpose, mobile ultrasound device that captures real-time images and analyzes body fluids across various clinical applications including obstetrics, cardiac, abdominal, and musculoskeletal imaging. It uses multiple imaging modes and some AI-based software features to assist clinicians in diagnosis and measurement, helping improve accuracy and efficiency in medical imaging.

Auto Lung Nodule Detection is a computer-aided detection software designed by Samsung Electronics to assist physicians in identifying and marking suspected pulmonary nodules between 10 and 30 mm on PA chest X-rays. Integrated into Samsung's digital X-ray systems, it serves as a second reader by highlighting regions of interest on images to support radiologists in their interpretation and improve detection accuracy.

Us2.v1 is an AI-powered software platform that automatically processes and analyzes transthoracic cardiac ultrasound images to measure key cardiac structural and functional parameters. It helps cardiologists and healthcare providers by producing comprehensive reports to support clinical decision-making for adult patients.

Chest-CAD is a computer-assisted detection software that uses machine learning to analyze adult chest X-rays. It identifies and highlights suspicious regions in various categories like cardiac, lungs, bones, and more, assisting physicians with concurrent reading during diagnosis. This helps improve detection accuracy and clinical workflow efficiency.

Precise Position is an AI-enabled accessory for Philips Incisive CT systems that assists technologists in accurately positioning patients before CT scans. It uses a ceiling-mounted camera to detect patient orientation, determine the scan range, and automatically center the patient's anatomy. This reduces positioning time and enhances accuracy for head, chest, abdomen, and pelvic CT imaging, ultimately improving imaging workflow and patient comfort.

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.