Prospective Evaluation of AI-ECG for SHD Detection

This study aims to improve the early detection of undiagnosed heart disease, which causes serious health issues, hospital admissions, and high healthcare costs. Researchers are exploring how artificial intelligence (AI) can analyse routine heart tests, called electrocardiograms (ECGs), to detect heart problems. These tests can be done using both traditional ECG machines and portable, wearable devices like smartwatches, making it easier for people to monitor their heart health at home. While AI has shown promise using past data, this study will involve the collection of ECG data and subsequent testing of its accuracy in real-world settings to ensure it works well for both doctors and patients. The goal is to see if AI can identify conditions like heart muscle weakness, valve issues, and high lung pressure from the ECG data of patients. The researchers will also compare AI's detections with other blood tests commonly used to diagnose heart disease. The AI models that will be used are being tested for research and validation purposes only. They will not be used for clinical decision-making or providing information to influence diagnosis, treatment, or patient care during the study. The AI outputs are not shared with clinicians and will have no impact on the care pathway. This research will demonstrate if AI-powered ECG analysis - whether from traditional or portable devices - can provide a low-cost, non-invasive way to detect heart disease early and improve health assessments.

NCT Number	NCT07057466
Lead Sponsor	Imperial College London
Conditions	Valvular Heart Disease Stenosis and Regurgitation (Diagnosis), Pulmonary Hypertension (Diagnosis), Heart Failure With Reduced Ejection Fraction (HFrEF; Diagnosis), Heart Failure With Preserved Ejection Fraction (HFpEF; Diagnosis)
Enrollment	590 participants
Start Date	2025-11-04
Primary Completion	2027-05-03 (estimated)
Study Completion	2027-08-02 (estimated)
Updated on ClinicalTrials.gov	2026-03-09