A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
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 resultin
| Condition(s) | Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary |
|---|---|
| Status | Recruiting |
| Phase | NA |
| Study type | Interventional |
| 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. |
| Who can participate | Inclusion Criteria: * Men or women, ≥ 50 to 85 years of age * At least one 12-lead ECG within 3 months Exclusion Criteria: * 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 |
| Ages | 50 Years to 85 Years |
| Sex | All |
| Lead sponsor | National Defense Medical Center, Taiwan |
| Locations | Taipei, Taiwan |
| Start date | 2026-02 |
| NCT ID | NCT07079592 |
| Official listing | https://clinicaltrials.gov/study/NCT07079592 |