AI analysis of ECGs to detect structural heart disease
Official title: Prospective Evaluation of Artificial Intelligence-enhanced Electrocardiography for Detection of Structural Heart Disease
Run by Imperial College London · NCT07057466 · Data updated 2026-11-12
This project will test whether AI can read traditional and wearable ECGs to find undiagnosed valve disease, pulmonary hypertension, and heart failure in adults.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 90 Years |
| Sex | All |
| Enrollment | 590 (estimated) |
| Sponsor | Imperial College London Academic / other |
| Locations | 6 sites (Bristol and 5 other locations) |
| Status | Recruiting |
| Started | November 4, 2025 |
| Main results expected | May 3, 2027 |
| Study ends | August 2, 2027 |
| Registry record updated | October 8, 2026 |
| Trial ID | NCT07057466 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are adults 18–90 who have no prior formal diagnosis of heart failure, pulmonary hypertension, or valvular heart disease and can provide informed consent.
✕Probably not a fit
Patients with prior diagnoses of heart failure, pulmonary hypertension, or valvular disease, those with unstable cardiovascular conditions, severe arrhythmias, or implanted pacemakers/ICDs are excluded and unlikely to benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational, prospective study will collect ECGs from routine 12‑lead machines and portable devices (Apple Watch, Eko Core, AliveCor) alongside blood tests (NT‑proBNP) to see how well AI models identify structural heart disease. Participants are adults aged 18–90 without prior formal diagnoses of heart failure, pulmonary hypertension, or valvular heart disease. Previously developed AI models trained on retrospective data will be applied to the newly collected ECGs to estimate detection accuracy in a real‑world setting. Results will be compared with standard biomarkers and clinical data to determine whether AI can reliably flag patients who need further cardiac evaluation.
Why it matters
Potential benefit: If successful, this approach could enable earlier and easier detection of serious heart conditions using routine or wearable ECGs, reducing missed diagnoses and preventing hospital admissions.
How similar studies have gone: Prior retrospective studies have shown promise for AI‑ECG detection of cardiac conditions, but prospective, real‑world validation remains limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria * Patients awaiting a transthoracic echocardiogram (TTE) based on clinical grounds as part of their standard care * Age 18-90 years * Able to provide informed consent Exclusion Criteria * Severe arrhythmia or unstable cardiovascular disease * Known severe structural heart disease, defined as any of the following: severe left ventricular systolic dysfunction (LVEF ≤35%); severe valvular heart disease according to current British Society of Echocardiography criteria; high echocardiographic probability of pulmonary hypertension according to current British Society of Echocardiography criteria; or previous structural cardiac intervention, including valve repair, valve replacement/implantation, or surgical repair of a cardiac chamber or congenital structural abnormality * Cardiac implantable electronic device in situ, including a permanent pacemaker or implantable cardioverter defibrillator * Current or recent involvement in another research study
Where it's running
-
Bristol Royal Infirmary
Bristol, United Kingdom Recruiting -
Southmead Hospital
Bristol, United Kingdom Recruiting -
Chelsea and Westminster Hospital
London, United Kingdom Recruiting -
Hammersmith Hospital
London, United Kingdom Recruiting -
St Mary's Hospital
London, United Kingdom Recruiting -
West Middlesex University Hospital
London, United Kingdom Recruiting
Study contacts
- Study coordinator: Ahmed YM El-Medany, MBChB, MRCP, MSc, FHEA
- Email: a.el-medany24@imperial.ac.uk
- Phone: +44 02075943614
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.