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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.
Sponsor: Yale University
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).
Official title: The Transthyretin Amyloid Cardiomyopathy Early Detection With Artificial Intelligence (TRACE-AI) Network Study
Key Details
Gender
All
Age Range
50 Years - 95 Years
Study Type
OBSERVATIONAL
Enrollment
1500000
Start Date
2025-01-24
Completion Date
2027-01
Last Updated
2025-07-14
Healthy Volunteers
No
Interventions
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)
Locations (11)
University of California - San Francisco (UCSF) Health
San Francisco, California, United States
Yale New Haven Health System
New Haven, Connecticut, United States
Henry Ford Health
Detroit, Michigan, United States
Mount Sinai
New York, New York, United States
Duke Health
Durham, North Carolina, United States
Providence Health
Tigard, Oregon, United States
Medical University of South Carolina (MUSC) Health
Charleston, South Carolina, United States
UT Southwestern Medical Center
Dallas, Texas, United States
Houstin Methodist
Houston, Texas, United States
University of Virginia School of Medicine
Charlottesville, Virginia, United States
University of Washington Medicine
Seattle, Washington, United States