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

Yale University

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.

Eligibility

Age range: 70+ years
Sex: All
Healthy volunteers: Yes

Detailed criteria

Inclusion Criteria: * Age 70 years or older * Attending a routine outpatient primary care clinic at one of the three enrollment sites Exclusion Criteria: * 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 implantabl…

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
Icahn School of Medicine at Mount Sinai
New York, New York
The Methodist Hospital Research Institute
Houston, Texas

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

Summary

Eligibility

Interventions

Locations (3)

Run this study?

Details

Conditions