A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension

NCT07079592 | Recruiting

• 1 other identifier: AI-PH
• Study Type: interventional
• Target: 8,666
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

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

Keywords

Artificial intelligence; electrocardiogram; deep learning; pulmonary hypertension

Outcome Measures

Primary Outcomes (1)

• Pulmonary arterial pressure > 50 mmHg

  The composite endpoint is defined as detecting pulmonary hypertension \> 50mmHg by echocardiography, indicating high risk for pulmonary hypertension.

  90 days

Secondary Outcomes (4)

• Left atrial enlargement on a parasternal long axis view

  Within 90 days after randomization.

• Left atrial enlargement by left atrium volume index

  Within 90 days after randomization.

• Right ventricular enlargement on a parasternal long axis view

  Within 90 days after randomization.

• New onset of left ventricular dysfunction

  Within 90 days after randomization.

Study Arms (2)

AI-ECG guidance

EXPERIMENTAL

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.

Diagnostic Test: AI-ECG Guidance

Standard clinical care

NO INTERVENTION

Participants in this arm are screened using the AI-ECG system, but diagnosis and management follow the usual clinical practice without echocardiography.

Interventions

AI-ECG Guidance DIAGNOSTIC_TEST

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.

AI-ECG guidance

Eligibility Criteria

• Age: 50 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

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

Sponsors & Collaborators

• National Defense Medical Center, Taiwan: lead

Study Sites (1)

National Defense Medical Center

Taipei, Taiwan

RECRUITING

Related Publications (1)

• 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 BACKGROUND

MeSH Terms

Conditions

Hypertension, Pulmonary

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Hypertension; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Chin Lin, associate professor

  National Defense Medical Center, Taiwan

  STUDY DIRECTOR

Central Study Contacts

Chin Lin, Associate Professor

CONTACT

886+2-87923311; up6fup0629@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant professor

Model Details: 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.

Study Record Dates

• First Submitted: July 14, 2025
• First Posted: July 23, 2025
• Study Start: February 1, 2026
• Primary Completion: June 15, 2026
• Study Completion: June 15, 2026
• Last Updated: February 24, 2026

Record last verified: 2025-12

Locations

TW (1)