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.

APPRAISE-HRI is a mobile health app designed for military healthcare providers that uses vital signs like heart rate and blood pressure collected from an external monitor to assess and stratify the risk of hemorrhage in trauma patients. This helps medics identify individuals who need urgent care or evacuation by providing a continuous hemorrhage risk score in real-time, improving situational awareness and clinical decisions post-trauma.

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 CLEWICU System is a software platform that uses AI machine learning models to predict the likelihood of future hemodynamic instability in adult patients in various hospital critical care units. It integrates patient data from electronic health records and monitoring devices to help clinicians assess a patient's risk of clinical deterioration or stability, providing valuable physiological insights as an aid to clinical judgment.

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.

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 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.