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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Aortic Stenosis
• Intervention / Treatment: Diagnostic test: Portable 1-lead electrocardiogram; Other: AI-ECG risk algorithm; Other: AI-POCUS; Diagnostic test: Point-of-care ultrasound

• Study Type: Interventional
• Enrollment (Estimated): 410
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Connecticut
    • New Haven, Connecticut, United States, 06519
      • Yale New Haven Health System
  • New York
    • New York, New York, United States, 10029
      • Icahn School of Medicine at Mount Sinai
  • Texas
    • Houston, Texas, United States, 77030
      • The Methodist Hospital Research Institute

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Older Adult
• Accepts Healthy Volunteers: Yes

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Intervention; 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; Portable 1-lead electrocardiogram (ECG) performed with the FDA-approved AliveCor KardiaMobile device.; Other: AI-ECG risk algorithm; Artificial intelligence (AI) risk algorithm for aortic stenosis using a 1-lead electrocardiogram; Other: AI-POCUS; Artificial intelligence (AI) risk algorithm for aortic stenosis using cardiac ultrasound plax videos.; Diagnostic test: Point-of-care ultrasound; Point-of-care ultrasound performed with the FDA-approved VScan Air device.
Sham Comparator: Control; 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; Portable 1-lead electrocardiogram (ECG) performed with the FDA-approved AliveCor KardiaMobile device.; Diagnostic test: Point-of-care ultrasound; Point-of-care ultrasound performed with the FDA-approved VScan Air device.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Until 12 months from the baseline visit

Collaborators and Investigators

• Sponsor: Yale University
• Collaborators: Icahn School of Medicine at Mount Sinai; National Institute on Aging (NIA); The Methodist Hospital Research Institute
• Investigators: Principal Investigator: Rohan Khera, MD, MS, Yale University

Study record dates

Study Major Dates

• Study Start (Actual): September 16, 2025
• Primary Completion (Estimated): August 31, 2028
• Study Completion (Estimated): August 31, 2028

Study Registration Dates

• First Submitted: December 19, 2024
• First Submitted That Met QC Criteria: December 19, 2024
• First Posted (Actual): December 27, 2024

Study Record Updates

• Last Update Posted (Actual): November 26, 2025
• Last Update Submitted That Met QC Criteria: November 25, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: artificial intelligence; aortic stenosis
• Additional Relevant MeSH Terms: Aortic Valve Disease; Cardiovascular Diseases; Heart Diseases; Heart Valve Diseases; Ventricular Outflow Obstruction; Aortic Valve Stenosis
• Other Study ID Numbers: 2000038634; R01AG089981 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: A de-identified dataset will be made available following publication of primary results.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE

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