AI analysis of ECGs to detect structural heart disease
Prospective Evaluation of Artificial Intelligence-enhanced Electrocardiography for Detection of Structural Heart Disease
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 |
|---|---|
| Enrollment | 590 (estimated) |
| Ages | 18 Years to 90 Years |
| Sex | All |
| Sponsor | Imperial College London Academic / other |
| Locations | 5 sites (Bristol and 4 other locations) |
| Trial ID | NCT07057466 on ClinicalTrials.gov |
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.
Who should consider this trial
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.
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.
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 performed: Prior retrospective studies have shown promise for AI‑ECG detection of cardiac conditions, but prospective, real‑world validation remains limited.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients aged 18-90 years * No prior formal diagnosis of HF (including systolic and diastolic dysfunction), PH, or VHD * Ability to provide informed consent Exclusion Criteria: * Severe arrhythmia or unstable cardiovascular disease * Prior formal diagnosis of HF (including systolic and diastolic dysfunction), PH, or VHD * Cardiac implantable electronic device in-situ, including a permanent pacemaker or implantable cardioverter defibrillator * Involvement in current research or recent involvement in any research prior to recruitment
Where this trial is running
Bristol and 4 other locations
- 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
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.