A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-ECG Guidance.
- Who it may be relevant to
- Registry conditions: Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary. Basic parameters: 50 years — 85 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Taiwan
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
Overview
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.
Detailed description
Pulmonary hypertension is often underdiagnosed due to extensive category of etiology. The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality.
Interventions
- Diagnostic test AI-ECG Guidance
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.
Primary outcome measures
- Pulmonary arterial pressure > 50 mmHg [Time frame: 90 days]
Secondary outcome measures (4)
- Left atrial enlargement on a parasternal long axis view [Time frame: Within 90 days after randomization.]
- Left atrial enlargement by left atrium volume index [Time frame: Within 90 days after randomization.]
- Right ventricular enlargement on a parasternal long axis view [Time frame: Within 90 days after randomization.]
- New onset of left ventricular dysfunction [Time frame: Within 90 days after randomization.]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Diagnostic
Study locations
Taiwan · 1 center
- National Defense Medical Center — Taipei
Publications
- Liu PY, Hsing SC, Tsai DJ, Lin C, Lin CS, Wang CH, Fang WH. A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension. J Imaging Inform Med. 2025 Apr;38(2):747-756. doi: 10.1007/s10278-024-01225-4. Epub 2024 Aug 13. PMID 39136826
Identifiers
NCT: NCT07079592 · AI-PH