Answers to Frequently Asked Questions

Yes, ECG-AI LEF is available now in the U.S. and is eligible for Medicare reimbursement in outpatient settings. To bring ECG-AI LEF to your facility, contact us.

Results are provided as a binary output within your EHR. When you view the ECG-AI™ LEF result within your EHR, the algorithm will inform you whether it has detected Low LVEF or not.

The ECG-AI LEF algorithm has been clinically validated in studies that enrolled patients across diverse populations, and has demonstrated 90.2% sensitivity and 85.1% specificity.

• Sensitivity: the ability to correctly identify patients with a disease/condition.
  • High sensitivity means few false-negative results, so fewer cases of disease are missed.

• Specificity: the ability of a test to correctly identify patients without the disease.
  • A high specificity means there are few false-positive results.

• AUROC measures the ability of a test to discriminate between two outcomes, in this case, EF ≤ 40% versus EF > 40%.
  • A perfect AUROC score is 100%, and an AUROC of ≥0.90 is considered excellent and better than most standard-of-care tests currently used to assess heart failure. For example, NT–proBNP, a common blood test used to help identify LEF today, has an AUROC of 0.7 – 0.8.¹

We enrolled a population of patients in our validation studies that closely mirrored the racial diversity of the U.S. population (including Asian, Black, Hispanic, and White ethnicities). The resulting sensitivity and specificity results were similar across the groups.

• HF is a progressive disease, and later stages are associated with increased mortality.
• If patients can be identified earlier, they can be treated earlier, potentially leading to better outcomes and improved quality of life.

Traditional 12-lead ECGs capture the electrical activity of the heart, but their interpretation often relies on linear rules and limited pattern recognition.
Anumana’s ECG-AI enhances ECG analysis by leveraging deep learning to uncover subtle and otherwise imperceptible signals and complex interdependencies. This breakthrough technology identifies difficult-to-diagnose cardiac conditions earlier, even before obvious symptoms or conventional ECG abnormalities emerge.