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Enrolling by Invitation

NCT Number: NCT06749145

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

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.

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Key information

Conditions

Aortic Stenosis Aortic Valve Disease Aortic Valve Stenosis Cardiovascular Diseases Heart Diseases Heart Valve Diseases Ventricular Outflow Obstruction

Age range

70 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Yale New Haven Health System, New Haven, Connecticut, United States

Loading trial locations.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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 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.

Treatment and study plan

Portable 1-lead electrocardiogram

Diagnostic Test

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

Point-of-care ultrasound

Diagnostic Test

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

AI-ECG risk algorithm

Other

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

AI-POCUS

Other

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

Primary outcomes

  1. Number of participants diagnosed with advanced aortic stenosis (AS) by transthoracic echocardiogram (TTE)

    Time frame: Until 12 months from the baseline visit

    The number of participants diagnosed with advanced AS by TTE at 12 months. Diagnosis of advanced AS is defined as diagnosis of moderate or severe AS as documented in the participant's electronic health record (EHR) at 12 months and adjudication of outcome via review of echocardiographic reports and videos performed by blinded members of the echocardiographic lab at the coordinating center.

Sponsors and collaborators

Lead sponsor

Yale University

Other

Collaborators

  • Icahn School of Medicine at Mount Sinai
  • National Institute on Aging (NIA)
  • The Methodist Hospital Research Institute

Registry information

Important dates

Study start
2025
Primary completion
2028
Study completion
2028
First posted
Dec 27, 2024
Registry last updated
Nov 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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