FDA Radiology AI Devices

Tempus ECG-AF is an AI-powered software that analyzes standard 12-lead ECG recordings from patients aged 65 and older to detect signs indicating an increased risk of atrial fibrillation or atrial flutter within the next 12 months. It assists clinicians by providing risk notifications based on ECG data, improving early identification of patients who may require further diagnostic follow-up.

Corvair is an AI-powered software that analyzes resting ECG recordings to detect various heart rhythms, morphological abnormalities, and measure ECG intervals. It supports healthcare professionals by providing an initial automated interpretation of ECGs to assist in cardiac diagnosis, enhancing accuracy and efficiency in clinical settings.

The CorVista System with PH Add-On is a non-invasive AI-based medical device that analyzes sensor-acquired cardiac electrical and hemodynamic signals to indicate the likelihood of elevated pulmonary arterial pressure, aiding clinicians in diagnosing pulmonary hypertension. It provides results through a user-friendly digital interface and supports healthcare providers by combining the AI output with clinical judgment.

Eko Low Ejection Fraction Tool (ELEFT) is an AI-based software device that analyzes ECG and heart sound recordings to help clinicians identify patients with reduced left ventricular ejection fraction (LVEF ≤ 40%), a marker of potential heart failure. It is intended for use on adults at risk of heart failure and supports referral decisions for further testing like echocardiography, without replacing diagnostic procedures.

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.

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.

The FaceHeart Vitals Software Development Kit (FH vitals SDK) is an AI-based software that measures pulse rate from facial video streams using standard cameras on mobile devices or computers. It provides non-invasive, real-time pulse rate monitoring for adults at rest, intended to assist healthcare professionals but not to replace critical care or continuous monitoring. The SDK uses face recognition and signal processing algorithms to produce accurate pulse rate measurements, helping clinicians monitor patient heart rate remotely or in general healthcare settings.

The Withings Scan Monitor 2.0 is a smart scale device that records a two-channel electrocardiogram (ECG) when a user stands on it and holds a handle. It analyzes the ECG data using AI algorithms to detect normal rhythms and atrial fibrillation, and displays results on the device and a mobile app, helping patients and clinicians monitor heart conditions conveniently at home or clinical settings.