AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

March 2, 2026 updated by: Jae K. Oh, M.D., Mayo Clinic

The Clinical Utility of Artificial Intelligence-enabled Electrocardiograms in the Outpatient Practice - Diagnosing Aortic Stenosis and Diastolic Dysfunction

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.
Device: AI-ECG Dashboard
Patients standard of care ECG's will be processed through the AI-ECG Dashboard
Diagnostic test: Point of care ultrasound (POCUS)
Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of patients with positive AI-ECG
Time Frame: Baseline
Positive AI-ECG will be determined by the sensitivity, specificity, positive predictive value, and negative predictive value.
Baseline
Number of studies with reasonable image quality in patients with positive AI-ECG
Time Frame: Baseline

Image quality will be determined by sonographers at the time of imaging and will be scored on a scale from 1-4:

  1. Excellent , sufficient for publication
  2. Good, sufficient for data analysis
  3. Fair, just enough for data analysis without complete views
  4. Poor, not usable for data analysis
Baseline

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of times the AI ECG and TTE (transthoracic echocardiogram) are statistically comparative
Time Frame: Baseline
Will be compared using parametric (2-sample t-test) and non-parametric tests (Wilcoxon rank sum test) for continuous variables, and the χ2 test or Fisher exact test for nominal variables. A p-value of < 0.05 will be categorized as significant for the statistical analysis
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mayo Clinic

Investigators

  • Principal Investigator: Jae Oh, M.D., Mayo Clinic

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 8, 2024

Primary Completion (Estimated)

March 1, 2027

Study Completion (Estimated)

March 1, 2027

Study Registration Dates

First Submitted

August 29, 2024

First Submitted That Met QC Criteria

August 29, 2024

First Posted (Actual)

August 30, 2024

Study Record Updates

Last Update Posted (Actual)

March 4, 2026

Last Update Submitted That Met QC Criteria

March 2, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 24-000100

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

Yes

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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