ECG-AI LEF

GUDID 00860007426407

The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12-lead ECG acquisition, and within seconds provides a prediction of likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the thirdparty software on a device such as a smartphone, tablet, or PC. The Low Ejection Fraction AIECG Algorithm was trained to predict Low LVEF using positive and control cohorts, and the prediction of Low LVEF in patients is generated using defined conditions and covariates. The Low Ejection Fraction AI-ECG Algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the Low Ejection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

Anumana, Inc.

Cardiac electrophysiology processing software

• Primary Device ID: 00860007426407
• NIH Device Record Key: 4d1e6d48-49ed-4998-af87-0d6e416721a2
• Commercial Distribution Status: In Commercial Distribution
• Brand Name: ECG-AI LEF
• Version Model Number: v.2.2.0 (1010)
• Company DUNS: 118190277
• Company Name: Anumana, Inc.
• Device Count: 1
• DM Exempt: false
• Pre-market Exempt: false
• MRI Safety Status: Labeling does not contain MRI Safety Information
• Human Cell/Tissue Product: false
• Device Kit: false
• Device Combination Product: false
• Single Use: false
• Lot Batch: true
• Serial Number: false
• Manufacturing Date: false
• Expiration Date: false
• Donation Id Number: false
• Contains Natural Rubber Latex: false
• Labeled No Natural Rubber Latex: false
• RX Perscription: true
• OTC Over-The-Counter: false

Device Identifiers

Device Issuing Agency	Device ID
GS1	00860007426407 [Primary]

FDA Pre-market Approvals/Notifications & deNovo

• Premarket Notification: K232699

FDA Product Code

• QYE: Reduced Ejection Fraction Machine Learning-Based Notification Software

Sterilization

• Steralize Prior To Use: false
• Device Is Sterile: false

Device Entry Metadata

• Public Version Status: New
• Device Record Status: Published
• Public Version Number: 1
• Public Version Date: 2024-01-23
• Device Publish Date: 2024-01-15

Devices Manufactured by Anumana, Inc.

• 00860007426445 - ECG-AI Low Ejection Fraction (LEF) 12-Lead algorithm: 2024-12-13
• 00860007426407 - ECG-AI LEF: 2024-01-23The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12-lead ECG acquisition, and within seconds provides a prediction of likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the thirdparty software on a device such as a smartphone, tablet, or PC. The Low Ejection Fraction AIECG Algorithm was trained to predict Low LVEF using positive and control cohorts, and the prediction of Low LVEF in patients is generated using defined conditions and covariates. The Low Ejection Fraction AI-ECG Algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the Low Ejection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.
• 00860007426407 - ECG-AI LEF: 2024-01-23 The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-bas