FDA Radiology AI Devices

Frontier X Plus is a wearable ECG event recorder that monitors heart rhythms by capturing single-channel ECG signals via a chest strap. It automatically analyzes the ECG data using an embedded algorithm to detect arrhythmias such as atrial fibrillation, bradycardia, and tachycardia. The device helps clinicians monitor and diagnose heart conditions in adult patients, particularly those with suspected or known cardiac issues, by storing and transferring ECG data for physician review.

The Affera Integrated Mapping System with the Impedance Localization Patch Kit is a computer-based diagnostic tool designed to assist clinicians in catheter-based cardiac electrophysiological mapping. It provides real-time visualization of catheters, maps cardiac signals, and helps guide therapeutic interventions by displaying detailed cardiac maps and signals, enhancing cardiac procedure accuracy and patient care.

HeartKey Rhythm is a software platform designed to analyze ECG data from various ambulatory monitoring devices such as Holters and ECG patches. Using a combination of machine learning and signal processing, the software detects heartbeats, assesses rhythm, and identifies arrhythmias to assist healthcare professionals in diagnosing cardiac conditions. It integrates through an API into other ECG management or monitoring systems and supports non-urgent clinical decision making.

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.