Artificial Intelligence (AI)ADDPHNCT07079592
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
A study testing AI-ECG Guidance in people with Artificial Intelligence (AI) and Artificial Intelligence (AI) in Diagnosis, run by National Defense Medical Center, Taiwan at 1 site in Taiwan.
Trial 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… Read more about this study
- Who is it for?
- Ages 50 to 85 years
- Conditions the study is for
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary
- What is being tested?
- AI-ECG Guidance
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 8,666 participants
- Where
- 1 siteTaiwan
- Study dates
- Feb 2026to 15 Jun 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Men or women, ≥ 50 to 85 years of age
- At least one 12-lead ECG within 3 months
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
National Defense Medical Center
Taipei
886+287923311 ext. 16118, up6fup0629@gmail.com
About this study
Official title: A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
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.
Read the 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
- Being tested
- AI-ECG Guidance
- Keywords
- Artificial intelligence, electrocardiogram, deep learning, pulmonary hypertension
- Medical subject headings
- Hypertension, Pulmonary
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-ECG GuidanceDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pulmonary arterial pressure > 50 mmHg
The composite endpoint is defined as detecting pulmonary hypertension > 50mmHg by echocardiography, indicating high risk for pulmonary hypertension.
Measured: 90 days
Secondary outcomes (4)
Left atrial enlargement on a parasternal long axis view
The endpoint measures the size of left atrium > 40mm on a parasternal long axis view by echocardiography.
Measured: Within 90 days after randomization.
Left atrial enlargement by left atrium volume index
The endpoint measures the size of left atrium volume index > 29 mL/m2 in sinus rhythm or > 40 mL/m2 in AF by echocardiography.
Measured: Within 90 days after randomization.
Right ventricular enlargement on a parasternal long axis view
The endpoint measures the size of right ventricular basal dimension > 27mm by echocardiography.
Measured: Within 90 days after randomization.
New onset of left ventricular dysfunction
The endpoint measures the number and proportion of LVEF < 50%.
Measured: Within 90 days after randomization.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Jul 2025
- Study startFeb 2026 (estimated)
- Primary completion15 Jun 2026 (estimated)
- Study completion15 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Artificial Intelligence (AI)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 8,666 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Defense Medical Center, Taiwan
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Pang-Yen, Liu, National Defense Medical Center, Taiwan
- Investigators
- Chin Lin, associate professor, Study director, National Defense Medical Center, Taiwan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07079592
- Sponsor's protocol ID
- AI-PH
- Acronym
- ADDPH
- Status last verified
- Dec 2025
- First submitted
- 14 Jul 2025
- Last updated
- 24 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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.
PubMed 39136826
Study at a glance
From the registry record, last updated by the sponsor on 24 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07079592
- Acronym / sponsor ID
- ADDPH / AI-PH
- Conditions
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary
- Keywords
- Artificial intelligence, electrocardiogram, deep learning, pulmonary hypertension
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 8,666 participants (estimated)
- Lead sponsor
- National Defense Medical Center, TaiwanUniversity, hospital or other
- Study start
- Feb 2026 (estimated)
- Primary completion
- 15 Jun 2026 (estimated)
- Study completion
- 15 Jun 2026 (estimated)
- First posted
- 23 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| AI-ECG guidance | Experimental | Diagnostic Test: AI-ECG GuidanceParticipants 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. |
| 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. |
Who may qualify
- Ages
- 50 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Men or women, ≥ 50 to 85 years of age
- At least one 12-lead ECG within 3 months
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-ECG GuidanceParticipants 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 |
Primary outcome
Pulmonary arterial pressure > 50 mmHg
Time frame: 90 days
The composite endpoint is defined as detecting pulmonary hypertension > 50mmHg by echocardiography, indicating high risk for pulmonary hypertension.
4 secondary outcomes
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Feb 2026.
- Background: PubMed 39136826Liu 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.
- Individual participant dataSharing planned: Undecided
Related trials for Artificial Intelligence (AI)
Enrolling by invitationObservational study
Safety, Feasibility, and Acceptability of the MINERVA Voice Conversational Agent for Emergency Department History-Taking
History Taking, Artificial Intelligence (AI)
Enrolling by invitationObservational study
Radiomics-Based Non-Invasive MRI Differentiation of Uterine Sarcomas and Fibroids
Uterine Fibroid, Uterine Sarcoma
Not yet recruitingObservational study
A Prospective Validation Study of Radiomics in the Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma
Uterine Fibroid, Uterine Sarcoma
Not yet recruiting
A Pragmatic Trial of AI-Assisted Discharge Summary Generation
Inpatient Internal Medicine Patients, Patient Discharge
RecruitingObservational study
Development and Demonstration of a Precision Prevention and Treatment System for Colorectal Tubular Adenomas Lesions
Colorectal Adenoma, Artificial Intelligence (AI)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Chin Lin, associate professor study director
National Defense Medical Center, Taiwan
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-ECG Guidance in people with Artificial Intelligence (AI) and Artificial Intelligence (AI) in Diagnosis, run by National Defense Medical Center, Taiwan at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 24 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Chin Lin, Associate Professor (886+2-87923311 ext. 16118, up6fup0629@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pulmonary arterial pressure > 50 mmHg (time frame: 90 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07079592, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Feb 2026.