Artificial Intelligence Software for Heart Disease

(DAISEA-ECG Trial)

Overseen By Marie-Gabrielle Lessard, MSc

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Montreal Heart Institute

Disqualifiers: Pediatrics, Unable to follow guidelines

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new tool, the DeepECG platform, which uses artificial intelligence to help doctors detect heart diseases more effectively. The study aims to determine whether family doctors can identify heart problems more accurately with this AI tool compared to without it. Adults who have not had heart disease follow-ups or have had a clear check-up may find this trial suitable. As an unphased study, this trial offers a unique opportunity to contribute to cutting-edge research that could enhance heart disease detection for everyone.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It is best to consult with the trial coordinators for specific guidance.

What prior data suggests that the DeepECG platform is safe for diagnosing heart disease?

Research shows that the DeepECG platform uses AI to identify heart problems. Studies indicate that AI can detect issues like irregular heartbeats and changes in heart size, making it a useful tool for early detection of heart disease.

Regarding safety, this AI software is neither a drug nor a medical device, so it doesn't carry the same safety risks. Instead, it analyzes heart data to assist doctors. No reports of harm have emerged from using AI in this way. The goal is to aid doctors in diagnosing heart problems more effectively, potentially leading to improved treatment choices.

Overall, using AI for heart health appears promising and safe based on current studies. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the DeepECG platform because it leverages artificial intelligence to enhance heart disease diagnosis and treatment recommendations. Unlike traditional methods that rely on standard ECG readings interpreted by medical professionals, DeepECG uses advanced algorithms to analyze heart data more precisely and quickly. This technology aims to provide more accurate and personalized insights, potentially leading to earlier detection and more effective management of heart conditions. By integrating AI, DeepECG stands out as a promising tool that might revolutionize how healthcare providers approach heart disease.

What evidence suggests that the DeepECG platform is effective for diagnosing heart disease?

Research has shown that the DeepECG platform effectively identifies heart problems. One study found that it uses a combination of AI tools to spot unusual heart patterns with high accuracy. Another study demonstrated its ability to predict future heart issues, such as heart failure and heart attacks. It also detects hidden heart conditions earlier than traditional methods, sometimes up to two years sooner. In this trial, some participants will receive diagnoses and recommendations using the DeepECG platform, while others will not. Overall, these findings suggest that DeepECG could help diagnose heart diseases more quickly and accurately. ^{1 2 6 7 8}

Are You a Good Fit for This Trial?

This trial is for individuals with heart disease being seen by primary care providers. Participants must be part of a Family Medicine Group (FMG) that's included in the study. Specific eligibility criteria are not provided, but typically participants would need to meet certain health conditions.

Inclusion Criteria

Family physicians who have given their free and informed consent

I am 18 or older and do not have ongoing heart issues requiring specialist care.

Family Physicians or Nurse Practitioners practicing in one of the participating FMGs

Exclusion Criteria

I am a family physician who only treats children.

Family physicians unable to follow the project guidelines

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Control Period

Family Medicine Groups operate without AI recommendations to establish baseline data

Varies by group

Intervention Period

Family Medicine Groups transition to using the DeepECG platform with AI recommendations

18 months

Follow-up

Participants are monitored for the effectiveness of AI recommendations in improving diagnostic sensitivity

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• DeepECG platform

Trial Overview

The DAISEA-ECG project tests if the DeepECG platform can help family doctors better detect heart diseases using AI algorithms. The study compares doctor's diagnostic sensitivity with and without AI assistance over different periods in a randomized sequence.

How Is the Trial Designed?

Group I: DeepECG plateform diagnosis & recommendations Experimental Treatment 1 Intervention

Group II: No DeepECG plateform diagnosis & recommendations Active Control 1 Intervention

Who Is Running the Clinical Trial?

Montreal Heart Institute

Lead Sponsor

Trials

125

Recruited

85,400+

Published Research Related to This Trial

An AI model was developed to automatically quantify coronary artery calcium (CAC) using deep learning on ECG-gated cardiac CT images, trained on 560 images and validated on 409 additional images, achieving excellent accuracy with a Cohen's kappa of 0.95 in the test cohort.

The AI model demonstrated minimal differences in CAC scores compared to manual evaluations, indicating its potential for reliable and efficient assessment of coronary artery health, which could enhance clinical decision-making.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method. Takahashi, D., Fujimoto, S., Nozaki, YO., et al.[2023]

Using electronic health records from 21,460 patients, a new personalized algorithm called ML4CAD was developed, which significantly improves health outcomes for managing Coronary Artery Disease (CAD) by predicting adverse events with an 81.5% accuracy.

The ML4CAD algorithm increases the expected time from diagnosis to adverse events by 24.11%, particularly benefiting male and Hispanic patients, and provides physicians with an interactive tool for personalized treatment recommendations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Personalized treatment for coronary artery disease patients: a machine learning approach. Bertsimas, D., Orfanoudaki, A., Weiner, RB.[2021]

The study emphasizes the need to expand cardiovascular safety assessments beyond just electrocardiogram events to include a wider range of cardiovascular parameters, using real-life case studies to demonstrate progress.

To enhance the prediction of cardiovascular events in patients, the development of more relevant humanized models is essential, allowing for better translation of findings to novel therapeutic approaches.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Current and future approaches to nonclinical cardiovascular safety assessment. Collins, TA., Rolf, MG., Pointon, A.[2021]

A study involving 44,959 patients demonstrated that an AI model applied to ECG data can effectively identify asymptomatic left ventricular dysfunction (ALVD), achieving high accuracy (85.7%) and sensitivity (86.3%).

Patients who screened positive for ALVD using the AI model were found to be four times more likely to develop future ventricular dysfunction, highlighting the potential of AI-enhanced ECG as a proactive screening tool for early intervention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Attia, ZI., Kapa, S., Lopez-Jimenez, F., et al.[2022]

Citations

1.

nature.com

nature.com/articles/s41598-025-07781-1

DeepECG-Net: a hybrid transformer-based deep learning ...

This research presents DeepECG-Net, a hybrid Transformer-CNN model for high-accuracy, robustness, and efficiency in real-time ECG anomaly detection.

2.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC11696183/

A multitask deep learning model utilizing ...

The model's performance achieves AUROCs of 0.90 for heart failure (HF), 0.85 for myocardial infarction (MI), 0.76 for ischemic stroke (IS), and ...

3.

ahajournals.org

ahajournals.org/doi/10.1161/CIR.0000000000001201

Use of Artificial Intelligence in Improving Outcomes in Heart ...

Application of AI/ML on the ECG appears effective in detecting occult structural heart disease up to 1 to 2 years earlier than traditional ...

4.

withpower.com

withpower.com/trial/phase-heart-diseases-11-2024-b03f7

Artificial Intelligence Software for Heart Disease (DAISEA ...

The DeepECG platform is unique because it uses artificial intelligence to analyze electrocardiogram (ECG) data for diagnosing heart conditions, offering a fully ...

5.

sciencedirect.com

sciencedirect.com/science/article/pii/S2589750024001729

Articles Artificial intelligence-enabled electrocardiogram for ...

The AIRE platform can also predict future cardiovascular events, such as atherosclerotic cardiovascular disease, heart failure, and ventricular ...

6.

clinicaltrials.gov

clinicaltrials.gov/study/NCT06462989

Harnessing ECG Artificial Intelligence for Rapid Treatment ...

Also, this platform will display the DeepECG-AI interpretation which detects problems such as ischemic conditions, arrhythmias or chamber enlargements and acts ...

7.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC11042786/

Use of Artificial Intelligence in Improving Outcomes in Heart ...

This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, ...

8.

biomedical-engineering-online.biomedcentral.com

biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-025-01349-w

Deep learning and electrocardiography: systematic review of ...

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes.