A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
Status: Recruiting
Location: See location...
Intervention Type: Diagnostic test
Study Type: Interventional
Study Phase: Not Applicable
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
Participation Requirements
Sex: All
Minimum Age: 50
Maximum Age: 85
Healthy Volunteers: f
View:
• Men or women, ≥ 50 to 85 years of age
• At least one 12-lead ECG within 3 months
Locations
Other Locations
Taiwan
National Defense Medical Center
RECRUITING
Taipei
Contact Information
Primary
Chin Lin, Associate Professor
up6fup0629@gmail.com
886+2-87923311
Time Frame
Start Date: 2026-02
Estimated Completion Date: 2026-06-15
Participants
Target number of participants: 8666
Treatments
Experimental: AI-ECG guidance
Participants in this arm 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.
No_intervention: Standard clinical care
Participants in this arm are screened using the AI-ECG system, but diagnosis and management follow the usual clinical practice without echocardiography.
Related Therapeutic Areas
Sponsors
Leads: National Defense Medical Center, Taiwan