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Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care (DAISEA-ECG)

Sponsor
Montreal Heart Institute (Academic or other)
Enrolment
2,000 planned
Conditions
Primary Care Provider; Structural Heart Disease
Interventions
DeepECG plateform diagnosis & recommendations
Ages
adults, older adults
Registry
NCT06637293

Full record on ClinicalTrials.gov

From the registry

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.

Primary outcomes

  • sensitivity of cardiology referrals

History

  1. Study completion expected

  2. Primary completion expected

  3. Last registry update

  4. Started

  5. Registered