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A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension

NCT ID: NCT07079592

Last Updated: 2025-12-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

8666 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-12-31

Study Completion Date

2026-06-15

Brief Summary

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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

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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

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

Keywords

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Artificial intelligence electrocardiogram deep learning pulmonary hypertension

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

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 Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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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.

Group Type EXPERIMENTAL

AI-ECG Guidance

Intervention Type 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.

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.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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
* No echocardiography in 3 months before index ECG
Minimum Eligible Age

50 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Defense Medical Center, Taiwan

OTHER

Sponsor Role lead

Responsible Party

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Pang-Yen, Liu

Assistant professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Chin Lin, associate professor

Role: STUDY_DIRECTOR

National Defense Medical Center, Taiwan

Central Contacts

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Chin Lin, Associate Professor

Role: CONTACT

Phone: 886+2-87923311

Email: [email protected]

References

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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.

Reference Type BACKGROUND
PMID: 39136826 (View on PubMed)

Other Identifiers

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AI-PH

Identifier Type: -

Identifier Source: org_study_id