ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010)

by Anumana  · Based in United States → — Unlocking the Language of the Heart & Transforming Cardiac Care Through Cutting-Edge AI Solutions.
Cardiology Emergency Medicine Internal Medicine
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Overview

Anumana’s ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm is an innovative, AI-driven software-as-a-medical device (SaMD) designed to enhance early diagnosis and intervention in cardiovascular care. Developed in partnership with Mayo Clinic, this technology analyzes data from a routine 12-lead electrocardiogram (ECG) to detect patterns invisible to the human eye, which are indicative of low ejection fraction (LEF) in adults at risk for heart failure. The algorithm integrates seamlessly into existing clinical workflows, transforming standard ECGs into a powerful tool for assessing left ventricular dysfunction. It aims to aid physicians in identifying hidden cardiovascular diseases earlier, thereby improving patient outcomes through timely interventions. Anumana’s platform is built on rigorous science and extensive clinical validation, leveraging large datasets of ECG and echocardiogram pairs. The company also offers a web-based ECG Viewer, a zero-footprint clinical dashboard that displays patient ECG waveforms, history, real-time AI results, and workflow tools integrated into the EHR.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • AI-powered analysis of 12-lead ECGs
  • Detects patterns invisible to the human eye
  • Screens for low ejection fraction (LEF)
  • Software-as-a-medical device (SaMD)
  • Integrates into existing clinical workflows
  • Aids in earlier detection of heart failure risk
  • Clinically validated with extensive data
  • Web-based ECG Viewer (zero-footprint clinical dashboard)
  • Supports clinical decision-making at the point of care
  • Established CPT codes and reimbursement pathways

Use Cases

  • Early identification of low ejection fraction (LEF) in at-risk adults
  • Screening for heart failure risk during routine care
  • Transforming standard 12-lead ECGs into a powerful diagnostic tool
  • Supporting clinical decision-making for cardiovascular care
  • Identifying hidden cardiovascular diseases
  • Improving patient outcomes through timely interventions

What Physicians Need to Know

Evidence Base
The ECG-AI LEF algorithm was developed in collaboration with Mayo Clinic and received FDA 510(k) clearance in September 2023, along with a Breakthrough Device designation. It is supported by a robust literature scope, including a landmark trial published in Nature Medicine and the pragmatic cluster randomized EAGLE trial. The algorithm has also been included in the Centers for Medicare & Medicaid Services (CMS) 2025 Hospital Outpatient Prospective Payment System final rule, facilitating reimbursement.
Clinical Validation Studies
The algorithm has been validated in over 25 studies involving more than 40,000 patients. A multi-site, external validation study of 13,960 subjects demonstrated an Area Under the Receiver Operating Characteristic (AUROC) of 0.92 (95% CI: 0.91-0.93), with a sensitivity of 84.5% (95% CI: 82.2%-86.6%) and specificity of 83.6% (95% CI: 82.9%-84.2%) for detecting low ejection fraction (LEF). The negative predictive value was 98.4% (95% CI: 98.2%-98.7%). The EAGLE trial (NCT04000087) prospectively evaluated the tool in primary care, showing that clinician access to AI-ECG data increased the diagnosis of low EF (2.1% vs 1.6%; OR 1.32; 95% CI 1.01-1.67), particularly among patients with positive AI-ECG results (19.5% vs 14.5%; OR 1.43; 95% CI 1.08-1.91).
Alert Fatigue Management
The EAGLE trial design included qualitative studies and post-implementation surveys to identify facilitators and barriers to using the screening report, indicating an awareness of potential alert fatigue and the need to optimize implementation. The algorithm delivers binary results via API without a direct graphical user interface, which can be integrated into existing systems, allowing for customizable alert presentations that may help manage fatigue.
Override Rate Data
In the EAGLE study, approximately half of the clinicians did not order a confirmatory echocardiogram even after a positive AI-ECG result, suggesting a significant non-action or 'override' rate. Further clinician surveys were planned to understand the rationale behind this.
Guideline Update Frequency
The device incorporates a Predetermined Change Control Plan, which allows for periodic software updates. These updates are validated by multicenter retrospective clinical studies using new data to continuously enhance sensitivity and specificity.
Clinical Workflow Integration
The algorithm is designed for seamless integration into existing clinical workflows, supporting point-of-care screening in primary, outpatient, and emergency department settings. It is delivered as a software module in a Docker container, integrating into third-party clinical systems like EMR or ECG management systems via API. Partnerships with Philips and AliveCor aim to embed the ECG-AI LEF into their respective ECG portfolios and devices.
Decision Audit Trail
While not explicitly detailed in the provided search results, as an FDA-cleared software as a medical device (SaMD) integrated into clinical systems, it is highly probable that a decision audit trail feature exists to ensure regulatory compliance and clinical accountability.
Physician Tip

The ECG-AI LEF algorithm serves as a valuable screening tool for early detection of low ejection fraction in at-risk adults, including those with cardiomyopathies, post-myocardial infarction, aortic stenosis, chronic atrial fibrillation, cardiotoxic therapies, and postpartum women. A positive result should prompt further clinical evaluation, ideally with a confirmatory transthoracic echocardiogram. Conversely, a negative result in a low-risk patient may help defer unnecessary echocardiography, but should not rule out further non-invasive evaluation if the patient remains at high risk. Always integrate the AI-ECG results with comprehensive clinical judgment and patient history, as it is not intended as a standalone diagnostic device.

The ECG-AI LEF algorithm is designed for broad compatibility, integrating as a software module into various third-party clinical systems, including Electronic Medical Records (EMR) and ECG management systems, through an API. It supports 12-lead ECG devices with 500 Hz digital output. Strategic partnerships, such as with Philips and AliveCor, facilitate its integration into widely used ECG platforms and devices, enhancing accessibility and streamlining its use within diverse healthcare IT environments.

Details

Category Cardiology AI, Clinical Decision Support & Reference
Pricing Contact for pricing
DeploymentCloud-based
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Anumana's ECG-AI LEF 12-Lead algorithm received U.S. FDA 510(k) clearance (K250652) on October 2, 2023, as a software-as-a-medical device (SaMD) to screen for low ejection fraction (LEF) in adults at risk for heart failure using data from a routine 12-lead electrocardiogram.
Integrations
EHR Not specified
Specialties Cardiology, Emergency Medicine, Internal Medicine

What the Web Says

The Anumana ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) is a promising AI tool designed to identify patients with LEF from a standard ECG. Reviews highlight its potential for early detection and improved patient outcomes, particularly in primary care settings, by flagging individuals who may need further echocardiogram evaluation. While generally well-received for its innovative approach, some discussions touch upon the need for integration into existing workflows and the ongoing validation in diverse clinical populations.

Overall: Positive

Strengths

  • Early detection of LEF from a readily available ECG, potentially improving patient outcomes.
  • Non-invasive and low-cost screening method.
  • Reduces the need for immediate echocardiograms in all suspected cases, optimizing resource allocation.
  • Potential to identify asymptomatic patients with LEF.
  • Can be integrated into existing clinical workflows.
  • Backed by significant clinical research and FDA authorization.

Limitations

  • Requires further validation in real-world, diverse clinical populations.
  • Integration into existing Electronic Health Record (EHR) systems can be a challenge.
  • Potential for false positives, leading to unnecessary follow-up tests.
  • Physician training and comfort with AI-driven diagnostics are necessary.
  • Reliance on high-quality ECG data for accurate results.
  • Cost of implementation and ongoing maintenance for healthcare providers.

Based on reviews from: Anumana.ai official website, Cardiology Today, Healthcare IT News, Journal of the American College of Cardiology (JACC), Reddit (r/medicine, r/healthcaretechnology), G2 (general AI/healthcare tech reviews), Capterra (general AI/healthcare tech reviews)

Last updated: 2026-07-17

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Press & Coverage

FDA
ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) Receives FDA Clearance
Anumana, Inc.'s ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) received FDA 510(k) clearance on July 28, 2025, categorized as 'Reduced Ejection Fraction Machine Learning-Based Notification Software'.
2025-07
Innolitics
2025 Year in Review: Cardiology AI/ML SaMD 510(k) Clearances
This article provides a comprehensive analysis of 2025 cardiology AI/ML clearances, highlighting Anumana's ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) as a representative example of devices cleared for EF estimation or low EF screening.
2026-01
Innolitics
QYE u00b7 Reduced Ejection Fraction Machine Learning-Based Notification Software u2014 FDA Product Code
This resource lists the Anumana, Inc. ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) under the FDA product code QYE, which is for Reduced Ejection Fraction Machine Learning-Based Notification Software, cleared on July 28, 2025.
2025-07
X-ray Interpreter
Anumana | Radiology AI Companies
Anumana's profile mentions the ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) as a released product, cleared by the FDA on July 28, 2025.
2025-07
Aletia Index
Aletia Index u2014 Assessment Listings
The Aletia Index, an AI/ML Medical Device Index, includes Anumana, Inc.'s ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm (1010) with a 510k regulatory status from the FDA, last synced in March 2026.
2026-03

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Frequently Asked Questions

The ECG-AI LEF algorithm is designed to analyze standard 12-lead ECGs, providing an immediate assessment for potential low ejection fraction. It can be integrated into existing ECG management systems, flagging patients who may benefit from further diagnostic evaluation like echocardiography, thereby streamlining initial screening processes.
The primary utility is to serve as a rapid screening tool to identify patients with suspected low ejection fraction who might otherwise be missed. Physicians should interpret a positive result as an indication for further confirmatory testing, such as an echocardiogram, rather than a definitive diagnosis, guiding earlier intervention and management strategies.
While specific performance metrics vary, these algorithms generally aim for high sensitivity to capture most at-risk patients, often with moderate specificity. Limitations can include reduced accuracy in patients with complex arrhythmias, significant ECG artifacts, or pre-existing conditions that alter ECG morphology independently of LEF, potentially leading to false positives or negatives.
For clinical use, the ECG-AI LEF algorithm (1010) must have appropriate regulatory clearance, such as FDA clearance in the United States or CE marking in Europe, indicating it meets safety and efficacy standards for its intended purpose. Physicians should verify the current regulatory status and adhere to local guidelines for medical device integration and use.
The ECG-AI algorithm is intended as a screening or risk stratification tool, not a replacement for definitive diagnostic methods like echocardiography, which remains the gold standard for assessing ejection fraction. It complements traditional methods by identifying patients who warrant further investigation, potentially reducing delays in diagnosis compared to relying solely on symptoms or biomarker levels.
Pricing models typically vary, ranging from per-use fees to subscription-based licenses, often dependent on the volume of ECGs processed or the number of integrated devices. The potential impact on cost-effectiveness stems from its ability to facilitate earlier identification of LEF, which can lead to timelier interventions, potentially reducing hospitalizations and improving long-term patient outcomes.
Technical requirements typically involve integration with existing ECG machines or hospital information systems, often requiring specific software interfaces or cloud connectivity. Minimal specific training is usually needed for clinicians on interpreting the algorithm's output as a screening tool, but IT and biomedical staff may require training for initial setup and maintenance.

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Suggest an Edit → | Last Verified: 2026-04-17 | First Added: 2026-04-17
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