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AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

NCT06580158 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2026-03-04

No results posted yet for this study

Summary

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.

Conditions

  • Aortic Stenosis
  • Diastolic Dysfunction

Interventions

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.

Sponsors & Collaborators

Principal Investigators

  • Jae Oh, M.D. · Mayo Clinic

Eligibility

Min Age
60 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-11-08
Primary Completion
2027-03-31
Completion
2027-03-31
FDA Device
Yes

Countries

  • United States

Study Locations

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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 NCT06580158 on ClinicalTrials.gov