Low Ejection Fraction detection model

JSON twin: https://www.healthaidb.com/software/low-ejection-fraction-detection-model.json

Company Name

Anumana

Product URL

https://www.anumana.com

Company URL

https://www.anumana.com

Categories

• clinical Care
• diagnostic Support
• cardiology
• Clinical
• Diagnostic

Summary

Anumana's ECG-AI LEF is an AI-driven algorithm that analyzes 12-lead ECGs to detect low ejection fraction (≤40%), aiding early heart failure diagnosis.

Description

ECG-AI LEF is a software-as-a-medical device developed by Anumana in collaboration with Mayo Clinic. It utilizes AI to interpret standard 12-lead ECG data, identifying low ejection fraction (≤40%) in adults at risk for heart failure. The algorithm was trained on over 100,000 ECG and echocardiogram pairs and validated in multiple studies involving over 40,000 patients, demonstrating high sensitivity and specificity. It integrates seamlessly into existing clinical workflows, providing real-time AI insights to clinicians. In October 2023, Anumana received FDA 510(k) clearance for ECG-AI LEF, and in November 2024, the Centers for Medicare & Medicaid Services (CMS) included the technology in the 2025 Hospital Outpatient Prospective Payment System (OPPS) final rule, expanding access to advanced cardiovascular care. ([anumana.ai](https://www.anumana.ai/ecg-ai-lef?utm_source=openai))

Api Available

unknown

Certifications

• FDA 510(k) clearance
• CE/MDR
• ISO 13485

Company Founding

2021

Company Offices

• United States

Compliance

• HIPAA
• FDA 510(k) clearance
• SOC 2
• ISO 27001

Customers

• Philips
• Mayo Clinic
• InfoBionic.Ai

Data Residency

US-only

Data Standards

• ECG data
• HL7
• FHIR

Deployment Model

• SaaS

Features

• AI-driven detection of low ejection fraction (EF) from 12-lead ECG data
• Integration with existing ECG information management systems
• Web-based ECG Viewer for real-time AI results
• FDA-cleared software as a medical device (SaMD)
• Clinical validation across diverse patient populations

Id

SW2666

Integration Partners

• Philips
• InfoBionic.Ai

Integrations

• ECG information management systems
• Electronic health records (EHR)

Languages Supported

• English

Last Updated

2025-10-11

License

Commercial

Market Segment

• Enterprise
• SMB

Optional Modules

Os Platforms

• Web

Pricing Details

Contact vendor for pricing information.

Pricing Model

Subscription

Privacy Features

• BAA available
• consent management
• anonymization
• data minimization

Product Code

SW2666

Product Name

Low Ejection Fraction detection model

Ratings

• "Anumana's ECG-AI LEF algorithm has significantly improved our early detection capabilities for heart failure.", "Integrating Anumana's technology into our clinical workflow was seamless and enhanced our diagnostic accuracy."

Regions Available

• United States
• Europe

Related Urls

Release Year

2023

Security Features

• Encryption
• RBAC
• SSO/SAML
• audit logs
• 2FA

Specialties

• Cardiology

Support Channels

• email
• phone
• chat
• ticketing
• community
• 24x7

System Requirements

Target Users

• Clinicians
• Cardiologists
• Healthcare Providers

Training Options

• documentation
• webinars
• live_online
• onsite
• certification

Type

product

User Reviews

• "Anumana's ECG-AI LEF algorithm has significantly improved our early detection capabilities for heart failure.", "Integrating Anumana's technology into our clinical workflow was seamless and enhanced our diagnostic accuracy."

Version

1.0

Alternatives

See related products

Canonical JSON

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  "product_name": "Low Ejection Fraction detection model",
  "company_name": "Anumana",
  "product_url": "https://www.anumana.com",
  "company_url": "https://www.anumana.com",
  "related_urls": [],
  "product_code": "SW2666",
  "summary": "Anumana's ECG-AI LEF is an AI-driven algorithm that analyzes 12-lead ECGs to detect low ejection fraction (≤40%), aiding early heart failure diagnosis.",
  "description": "ECG-AI LEF is a software-as-a-medical device developed by Anumana in collaboration with Mayo Clinic. It utilizes AI to interpret standard 12-lead ECG data, identifying low ejection fraction (≤40%) in adults at risk for heart failure. The algorithm was trained on over 100,000 ECG and echocardiogram pairs and validated in multiple studies involving over 40,000 patients, demonstrating high sensitivity and specificity. It integrates seamlessly into existing clinical workflows, providing real-time AI insights to clinicians. In October 2023, Anumana received FDA 510(k) clearance for ECG-AI LEF, and in November 2024, the Centers for Medicare & Medicaid Services (CMS) included the technology in the 2025 Hospital Outpatient Prospective Payment System (OPPS) final rule, expanding access to advanced cardiovascular care. ([anumana.ai](https://www.anumana.ai/ecg-ai-lef?utm_source=openai))",
  "categories": [
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    "diagnostic Support",
    "cardiology",
    "Clinical",
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  "market_segment": [
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  "target_users": [
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    "Cardiologists",
    "Healthcare Providers"
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  "specialties": [
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  "regions_available": [
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  "languages_supported": [
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  "pricing_model": "Subscription",
  "pricing_details": "Contact vendor for pricing information.",
  "license": "Commercial",
  "company_offices": [
    "United States"
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  "company_founding": "2021",
  "deployment_model": [
    "SaaS"
  ],
  "os_platforms": [
    "Web"
  ],
  "features": [
    "AI-driven detection of low ejection fraction (EF) from 12-lead ECG data",
    "Integration with existing ECG information management systems",
    "Web-based ECG Viewer for real-time AI results",
    "FDA-cleared software as a medical device (SaMD)",
    "Clinical validation across diverse patient populations"
  ],
  "optional_modules": [],
  "integrations": [
    "ECG information management systems",
    "Electronic health records (EHR)"
  ],
  "data_standards": [
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  "api_available": "unknown",
  "system_requirements": "",
  "compliance": [
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    "FDA 510(k) clearance",
    "SOC 2",
    "ISO 27001"
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  "certifications": [
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    "CE/MDR",
    "ISO 13485"
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  "security_features": [
    "Encryption",
    "RBAC",
    "SSO/SAML",
    "audit logs",
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  "privacy_features": [
    "BAA available",
    "consent management",
    "anonymization",
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  "data_residency": "US-only",
  "customers": [
    "Philips",
    "Mayo Clinic",
    "InfoBionic.Ai"
  ],
  "user_reviews": [
    "\"Anumana's ECG-AI LEF algorithm has significantly improved our early detection capabilities for heart failure.\", \"Integrating Anumana's technology into our clinical workflow was seamless and enhanced our diagnostic accuracy.\""
  ],
  "ratings": [
    "\"Anumana's ECG-AI LEF algorithm has significantly improved our early detection capabilities for heart failure.\", \"Integrating Anumana's technology into our clinical workflow was seamless and enhanced our diagnostic accuracy.\""
  ],
  "support_channels": [
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  "training_options": [
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    "certification"
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  "release_year": "2023",
  "integration_partners": [
    "Philips",
    "InfoBionic.Ai"
  ],
  "id": "SW2666",
  "slug": "low-ejection-fraction-detection-model",
  "type": "product",
  "version": "1.0",
  "last_updated": "2025-10-11",
  "links_json": {
    "self": "https://www.healthaidb.com/software/low-ejection-fraction-detection-model.json"
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}