A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
Recruiting now · Not applicable
Conditions studied: Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary
In brief
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.
Key facts
- Study ID
- NCT07079592
- Run by
- National Defense Medical Center, Taiwan
- People needed
- 8666
- Starts
- 2026-02-01
- Expected to finish
- 2026-06-15
- Last updated by the study team
- 2026-02-24
Who can join
Age: 50 and older, up to 85. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- Men or women, ≥ 50 to 85 years of age
- At least one 12-lead ECG within 3 months
You may not qualify if…
- 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
Where it is running
- National Defense Medical Center — Taipei, Taiwan (enrolling)
Full record on ClinicalTrials.gov
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