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

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.

NCT Number	NCT06637293
Lead Sponsor	Montreal Heart Institute
Conditions	Primary Care Provider, Structural Heart Disease
Enrollment	2,000 participants
Start Date	2025-10-06
Primary Completion	2027-01 (estimated)
Study Completion	2027-03 (estimated)
Updated on ClinicalTrials.gov	2025-09-19