NCT07079592

A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension

A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 8,666 (estimated)
• Sponsor: National Defense Medical Center, Taiwan · Academic / Other
• Sex: All
• Age: 50 Years – 85 Years
• Healthy volunteers: Not accepted

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.

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.

Conditions

• Artificial Intelligence (AI)
• Artificial Intelligence (AI) in Diagnosis
• Hypertension, Pulmonary

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2026-02-01
• Primary completion: 2026-06-15
• Completion: 2026-06-15
• First posted: 2025-07-23
• Last updated: 2026-02-24

Locations

1 site across 1 country: Taiwan