Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.
A software program designed to add specific image processing and/or data analysis capabilities to a computer for the interpretation and/or screening of cardiac electrophysiology parameters [e.g., electrocardiogram (ECG)]. It is intended for use exclusively by healthcare professionals and may provide risk assessment for cardiac events [e.g., acute myocardial infarction (AMI), acute myocardial ischemic injury (AMII)] or screen for specific conditions (e.g., low ejection fraction). It might include artificial intelligence (AI) and machine learning (ML) technology.
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