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AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction
The Clinical Utility of Artificial Intelligence-enabled Electrocardiograms in the Outpatient Practice - Diagnosing Aortic Stenosis and Diastolic Dysfunction
Who can join
Ages 60 and older · All sexes
Full eligibility criteria
Inclusion Criteria: * ≥ 60 years of age must have a clinical scheduled ECG performed. Exclusion Criteria: * \< 59 years of age * Is not scheduled for a clinical ECG * Unable to provide consent.
About the study
Two recently developed artificial intelligence-enabled electrocardiogram (AI-ECG) models have been developed to detect aortic stenosis (AS) and diastolic dysfunction (DD). AI-ECG for AS has a sensitivity of 78% and specificity of 74%, and AI-ECG for DD has a sensitivity of 83% and specificity of 80%. However, these models have never been prospectively applied to diagnose AS or DD, which may be useful for patients and providers from a diagnostic and prognostic perspective and especially in settings where access to higher- level medical care is limited. In this study, we aim to determine the clinical utility of these AI-ECG models by prospectively applying them to an outpatient cohort and then completing a focused point-of-care ultrasound to evaluate those who are AI-ECG positive for AS and DD.
What is being tested
- AI-ECG Dashboard (device)
- Point of care ultrasound (POCUS) (diagnostic test)
Sponsor: Mayo Clinic · Participants: 2,000 · Started: Nov 8, 2024
Contact the study team
- Brian Rudquist · Phone: (507) 538-5146
Official record on ClinicalTrials.gov — NCT06580158
Locations in the U.S.
| Minnesota | Mayo Clinic, Rochester |
Conditions
From ClinicalTrials.gov, data retrieved Sep 30, 2026. Each study sets its own eligibility; the study team decides who can join.