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

NCT06749145 · Status: ENROLLING_BY_INVITATION · Phase: NA · Type: INTERVENTIONAL · Enrollment: 410

Last updated 2025-11-26

No results posted yet for this study

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

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.

Sponsors & Collaborators

  • National Institute on Aging (NIA)

    collaborator NIH

  • Icahn School of Medicine at Mount Sinai

    collaborator OTHER

  • The Methodist Hospital Research Institute

    collaborator OTHER

  • Yale University

    lead OTHER

Principal Investigators

  • Rohan Khera, MD, MS · Yale University

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
70 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-09-16
Primary Completion
2028-08-31
Completion
2028-08-31
FDA Device
Yes

Countries

  • United States

Study Locations

More Related Trials

Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06749145 on ClinicalTrials.gov