Deep Learning Enhanced Detection of Aortic Stenosis - The DETECT-AS-Diagnostic Study

Enrolling by invitation · Not applicable

Conditions studied: Aortic Stenosis

In brief

The DETECT-AS Diagnostic Study will assess the performance of artificial intelligence (AI) risk predictions to detect aortic stenosis using results from portable electrocardiogram (ECG) and cardiac ultrasound devices.

Key facts

Study ID

NCT06749145

Run by

Yale University

People needed

410

Starts

2025-09-16

Expected to finish

2028-08-31

Last updated by the study team

2025-11-26

Who can join

Age: 70 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• Age 70 years or older
• Attending a routine outpatient primary care clinic at one of the three enrollment sites

You may not qualify if…

• Opted out of research studies
• Non-English speaking
• Urgent or emergent visits, defined as a visit for an illness or injury that needs attention quickly or is life-threatening
• Any echocardiogram within 12 months of clinic visit
• Prior history of moderate or severe AS
• Prior history of aortic valve replacement or repair, including transcatheter and surgical AVR with either a bioprosthetic or mechanical valve
• Presence of implantable cardiac devices, including permanent cardiac pacer, implantable cardioverter-defibrillator, or left ventricular assist device
• Prior heart transplant
• History of dementia
• Documented life expectancy of <1 year or current participation in hospice services.

Where it is running

• Yale New Haven Health System — New Haven, Connecticut, United States
• Icahn School of Medicine at Mount Sinai — New York, New York, United States
• The Methodist Hospital Research Institute — Houston, Texas, United States

Full record on ClinicalTrials.gov

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