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Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care

NCT06637293 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 2000

Last updated 2025-09-19

No results posted yet for this study

Summary

The DAISEA-ECG project aims to improve the diagnosis of heart diseases in primary care through the DeepECG platform, which combines ECG-AI and ECHONeXT algorithms. This study uses a stepped wedge design, where each Family Medicine Group acts as its own control. The FMGs will gradually transition from the control period (without AI recommendations) to the intervention period (with AI recommendations activated) in a randomized sequence.

The primary objective is to compare the sensitivity of family physicians in detecting cardiac pathologies, with and without the assistance of the DeepECG platform. Sensitivity is defined as the proportion of patients correctly referred to cardiology or for transthoracic echocardiography (TTE) among those who indeed required cardiovascular evaluation, as confirmed by an independent adjudication committee.

Conditions

  • Primary Care Provider
  • Structural Heart Disease

Interventions

DEVICE

DeepECG plateform diagnosis & recommendations

EchoNeXT\& ECG-AI algorithm

Sponsors & Collaborators

  • Montreal Heart Institute

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-10-06
Primary Completion
2027-01-31
Completion
2027-03-31

Countries

  • Canada

Study Locations

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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 NCT06637293 on ClinicalTrials.gov