A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
National Defense Medical Center, Taiwan
8,666 participants
Feb 1, 2026
INTERVENTIONAL
Conditions
Summary
This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.
Eligibility
Inclusion Criteria2
- Men or women, ≥ 50 to 85 years of age
- At least one 12-lead ECG within 3 months
Exclusion Criteria5
- A diagnosis of PH WHO Groups 1, 2, 3, 4, or 5
- A diagnosis of hypertrophic cardiomyopathy, restrictive cardiomyopathy, constrictive pericarditis, cardiac amyloidosis, or infiltrative cardiomyopathy
- Prior heart, lung, or heart-lung transplants
- Any systolic pulmonary artery pressure \>50 mmHg by echocardiography before
- Echocardiography in 3 months before index ECG
Interventions
Participants undergo screening using the AI-ECG system. Those identified as high-risk for pulmonary hypertension receive echocardiography to confirm the diagnosis and guide subsequent management.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07079592