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ENROLLING BY INVITATION
NCT06749145
NA

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

Sponsor: Yale University

View on ClinicalTrials.gov

Summary

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 Details

Gender

All

Age Range

70 Years - Any

Study Type

INTERVENTIONAL

Enrollment

410

Start Date

2025-09-16

Completion Date

2028-08-31

Last Updated

2025-11-26

Healthy Volunteers

Yes

Conditions

Interventions

DIAGNOSTIC_TEST

Portable 1-lead electrocardiogram

Portable 1-lead electrocardiogram (ECG) performed with the FDA-approved AliveCor KardiaMobile device.

DIAGNOSTIC_TEST

Point-of-care ultrasound

Point-of-care ultrasound performed with the FDA-approved VScan Air device.

OTHER

AI-ECG risk algorithm

Artificial intelligence (AI) risk algorithm for aortic stenosis using a 1-lead electrocardiogram

OTHER

AI-POCUS

Artificial intelligence (AI) risk algorithm for aortic stenosis using cardiac ultrasound plax videos.

Locations (3)

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