FDA Radiology AI Devices

AI Platform (AIP001) by Exo Inc is a software tool designed to help medical professionals assess ultrasound images of the heart and lungs in adult patients. It uses AI to detect and measure features of these images, aiding in quantification and reporting of important clinical parameters such as ejection fraction and lung artifacts, thus supporting diagnosis and treatment decisions in a clinical setting.

The ACUSON Sequoia, Sequoia Select, and Origin are advanced multi-purpose diagnostic ultrasound systems by Siemens, designed for a wide range of clinical applications including fetal, abdominal, cardiac, vascular, and musculoskeletal imaging. They incorporate AI-based software applications (AI Measure, AI Assist, 2D HeartAI, 4D HeartAI) to assist clinicians in cardiac imaging by automating measurements, image annotation, and analysis, thereby improving workflow and diagnostic confidence.

The Philips Lumify Diagnostic Ultrasound System is a portable ultrasound device used by healthcare professionals to capture high-resolution ultrasound images and perform fluid flow analysis in various clinical applications including cardiac imaging. It incorporates an AI-based Auto EF Quantification feature that automatically assesses cardiac function by measuring the left ventricle’s ejection fraction, improving clinical workflows during cardiac examinations.

Sonix Health is a software product that assists clinicians in analyzing adult echocardiography ultrasound images. It uses artificial intelligence to automatically classify ultrasound views, segment anatomy, and perform semi-automated measurements, helping physicians quantify cardiac function more efficiently. The software generates reports based on B-mode, M-mode, and Doppler ultrasound images and supports manual verification and adjustment of AI results.

SpotLight/SpotLight Duo (with DLIR option) is a CT scanner that produces cross-sectional images of the body, including cardiovascular and thoracic anatomies. It uses a dual-tube CT system and advanced deep learning reconstruction to enhance image quality while reducing noise. This helps clinicians obtain clearer diagnostic images for disease detection and therapy planning.

The Low Ejection Fraction AI-ECG Algorithm by Anumana, Inc. is a software tool that analyzes 12-lead ECG signals using AI to aid in screening for patients with low left ventricular ejection fraction (≤ 40%). It helps clinicians identify adults at risk of heart failure to decide if further cardiac evaluation is needed. It works quickly on routine ECGs and does not replace diagnostic imaging but supports clinical judgment.

Volta AF-Xplorer is a medical software device that helps doctors analyze and annotate electrical signals from the heart in real-time. It uses machine learning to detect areas in the atria that exhibit abnormal electrical patterns during atrial fibrillation or tachycardia, supporting catheter ablation procedures. This software integrates with cardiac mapping systems to improve the precision and efficiency of cardiac electrophysiology interventions.

The CorVista System is a non-invasive medical device combining hardware and software to analyze physiological signals from patients with cardiovascular symptoms. Using machine learning, it provides healthcare providers with a likelihood of significant coronary artery disease to aid diagnosis, integrating clinical judgment and patient history. This helps clinicians identify patients who may need further evaluation or treatment.

Cleerly ISCHEMIA is an AI-based software tool that analyzes coronary CT angiography images to help clinicians detect likely ischemia in coronary vessels. It works as an add-on to Cleerly Labs software, providing a non-invasive decision support tool to assess the functional significance of coronary artery disease, aiding patient management alongside traditional diagnostic methods.

The CARTO™ 3 EP Navigation System Software V8.0 is a catheter-based cardiac mapping system that helps clinicians map the electrical activity and anatomy of the heart in 3D during electrophysiological procedures. Using specialized catheters and advanced mapping technologies including machine learning algorithms, it improves visualization and identification of complex heart signals to guide interventions and improve procedural outcomes.