An interventional study of Portable 1-lead electrocardiogram and Point-of-care ultrasound in Aortic Stenosis, sponsored by Yale University. Enrolling by invitation at 3 sites in United States. Open to participants aged 70 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-06.
Sponsored by Yale University · Not applicable, Interventional, and Diagnostic
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.
985 studies on the registry are indexed under Aortic Valve Stenosis; 283 are open to participants now.
This study's planned enrollment of 410 is above the median of 120 across 525 interventional studies indexed under Aortic Valve Stenosis.
Browse Aortic Valve Stenosis studies →Yale University is the lead sponsor of 1,724 studies on the registry; 298 are open to participants now.
Of its 210 completed or terminated interventional studies of FDA-regulated products, 126 (60%) have results posted.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
The intervention arm will undergo sequential screening for aortic stenosis using portable 1-lead electrocardiograms (ECGs), followed by point-of-care ultrasound (POCUS), if indicated, by artificial intelligence (AI)-based risk algorithms.
Diagnostic Test: Portable 1-lead electrocardiogram · Diagnostic Test: Point-of-care ultrasound · Other: AI-ECG risk algorithm · Other: AI-POCUS
The control arm will undergo a portable 1-lead electrocardiogram (ECG), with 10% randomly assigned to undergo point-of-care ultrasound (POCUS).
Diagnostic Test: Portable 1-lead electrocardiogram · Diagnostic Test: Point-of-care ultrasound
Portable 1-lead electrocardiogram (ECG) performed with the FDA-approved AliveCor KardiaMobile device.
Point-of-care ultrasound performed with the FDA-approved VScan Air device.
Artificial intelligence (AI) risk algorithm for aortic stenosis using a 1-lead electrocardiogram
Artificial intelligence (AI) risk algorithm for aortic stenosis using cardiac ultrasound plax videos.
Number of participants diagnosed with advanced aortic stenosis (AS) by transthoracic echocardiogram (TTE)
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.
Time frame: Until 12 months from the baseline visit
Plan to share: Yes — A de-identified dataset will be made available following publication of primary results.
Supporting information: Study protocol, Sap, Icf, Analytic code
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.
No contact was published for this record. The registry link below has the sponsor’s details.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Yale University