ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010)
Anumana, Inc.The ECG-AI Low Ejection Fraction (LEF) 12-Lead Algorithm is an AI-based software intended to analyze standard 12-lead ECG data to detect patients with low left ventricular ejection fraction (LVEF ≤ 40%). This aids clinicians in early detection of potential heart failure in adults at risk, helping guide timely further cardiac evaluation. It works by analyzing electrical signals from the heart recorded in an ECG to assess cardiac function, providing a quick, non-invasive screening tool compatible with various ECG devices.
Intended Use
Software intended to aid in earlier detection of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure, including patients with cardiomyopathies, post-myocardial infarction, aortic stenosis, chronic atrial fibrillation, cardiotoxic pharmaceutical therapies, and postpartum women.
Technology
AI-based software interprets 12-lead ECG voltage time series data from a 10-second acquisition using a machine learning algorithm to output the likelihood of low LVEF (≤ 40%). Delivered as a software module in a Docker container, it integrates into third-party clinical systems (EMR or ECG management systems) and provides binary results via API without a direct GUI. It supports 12-lead ECG devices with 500 Hz digital output.
Performance
The device underwent software verification and labeling verification. It includes a Predetermined Change Control Plan allowing periodic software updates validated by multicenter retrospective clinical studies with new data to enhance sensitivity and specificity. Equivalence to predicate device demonstrated by non-clinical verification, ensuring safety and effectiveness.
Predicate Devices
No predicate devices specified
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