NCT06749145

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

• Status: Enrolling By Invitation
• Phase: N/A
• Study type: Interventional
• Enrollment: 410 (estimated)
• Sponsor: Yale University · Academic / Other
• Sex: All
• Age: 70 Years
• Healthy volunteers: Accepted

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.

Conditions

• Aortic Stenosis

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2025-09-16
• Primary completion: 2028-08-31
• Completion: 2028-08-31
• First posted: 2024-12-27
• Last updated: 2025-11-26

Locations

3 sites across 1 country: United States

Regulatory

• FDA-regulated device study