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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

2000 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-10-06

Study Completion Date

2027-03-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Identification of Structural Heart Disease

NCT06462989

Structural Heart Abnormality Structural Heart Disease

ENROLLING_BY_INVITATION NA

Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)

NCT06009718

Heart Failure

RECRUITING

Artificial Intelligence-assisted Diagnosis and Prognostication in Low Ejection Fraction Using Electrocardiograms

NCT05117970

Left Ventricular Systolic Dysfunction (Disorder)

COMPLETED NA

A Multicenter Pragmatic Implementation Study of ECG-AI-Based Clinical Decision Support Software to Identify Low LVEF

NCT05867407

Ventricular Ejection Fraction

TERMINATED NA

Detection of Reduced Left Ventricular Ejection Fraction With Three-Lead ECG Using Artificial Intelligence

NCT07270692

Low Ejection Fraction

RECRUITING

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Mathematically, sensitivity is calculated as True Positive / (True Positive + False Negative), where True Positive represents correctly referred patients and false negatives represents patients who should have been referred but were not.

The secondary objectives include determining the rate of cardiovascular evaluation referrals before and after the intervention (implementation of the DeepECG platform), the individual characteristics of the intervention (PPV, NPV, and specificity), as well as evaluating the feasibility of implementing AI-based automatic ECG interpretation in primary care through surveys of family physicians and cardiologists.

PPV: Positive predictive value NPV: Negative predictive value

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Primary Care Provider Structural Heart Disease

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

stepped wedge randomization

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Outcome Assessors

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

No DeepECG plateform diagnosis & recommendations

Group Type NO_INTERVENTION

No interventions assigned to this group

DeepECG plateform diagnosis & recommendations

Group Type EXPERIMENTAL

DeepECG plateform diagnosis & recommendations

Intervention Type DEVICE

EchoNeXT\& ECG-AI algorithm

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

DeepECG plateform diagnosis & recommendations

EchoNeXT\& ECG-AI algorithm

Intervention Type DEVICE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

Family Physicians or Nurse Practitioners

Family physicians or nurse practitioners (NPs) practicing in one of the participating FMGs.

Family physicians who have given their free and informed consent. Patients

Adult patients (18 years or older). Patients without follow-up in cardiology or internal medicine for cardiovascular issues (arrhythmia, heart failure, myocardial infarction, atherosclerotic coronary artery disease, valvular heart disease) or those who had a negative investigation in the past with no additional follow-up.

ECG

Any 12-lead ECG performed with the MUSE GE 360 machine. ECG of adequate technical quality for interpretation (otherwise, it will be automatically rejected by the platform).

\-

Exclusion Criteria

* Family Physicians or Nurse Practitioners

Family physicians practicing exclusively in pediatrics (patients under 18 years old).

Family physicians unable to follow the project guidelines.

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes