NCT07062848

An Observational Study Using Artificial Intelligence (AI) Algorithms on Electrocardiography (ECG), Point-of-care Ultrasound (POCUS), and Transthoracic Echocardiophy (TTE) to Estimate the Under-diagnosis of Transthyretin Amyloid Cardiomyopathy (ATTR-CM) Across a Diverse Range of US Health Systems.

The Transthyretin Amyloid Cardiomyopathy Early Detection With Artificial Intelligence (TRACE-AI) Network Study

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,500,000 (estimated)
• Sponsor: Yale University · Academic / Other
• Sex: All
• Age: 50 Years – 95 Years
• Healthy volunteers: Not accepted

Summary

This is a multi-center, observational study with the overall objective to examine the scale of under-diagnosis for transthyretin amyloid cardiomyopathy (ATTR-CM) across a broad range of diverse health systems in the US using a fully federated deployment of an artificial intelligence (AI) toolkit of algorithms that detect ATTR-CM on electrocardiography (ECG), point-of-care ultrasound (POCUS), and transthoracic echocardiography (TTE).

Conditions

• Transthyretin (TTR) Amyloid Cardiomyopathy

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	AI Toolkit for ATTR-CM Diagnosis	An artificial intelligence (AI) toolkit of algorithms that detect ATTR-CM on electrocardiography (ECG), point-of-care ultrasound (POCUS), and transthoracic echocardiography (TTE)

Timeline

• Start date: 2025-01-24
• Primary completion: 2027-01-01
• Completion: 2027-01-01
• First posted: 2025-07-14
• Last updated: 2025-07-14

Locations

11 sites across 1 country: United States