Pulmonary HypertensionAIPHYNCT05566002
Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension
An observational study of Pulmonary Hypertension and Pulmonary Arterial Hypertension, run by Chinese Pulmonary Vascular Disease Research Group at 1 site in China.
Trial summary
Pulmonary hypertension represents a challenging and heterogeneous condition that is associated with high mortality and morbidity if left untreated. Artificial intelligence is used to study and develop theories and methods that simulate and extend human intelligence, which is being applied in fields related to… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pulmonary Hypertension, Pulmonary Arterial Hypertension
- What is being tested?
- Right heart catheterization
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2022to 31 Dec 2028
- Phase
- Observational
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adult patients previously received chest X-rays, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China.
Who may be able to join
- Age ≥18 years old
- Patients previously received chest X-ray, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China
Who cannot join
- Patients without RHC
- The quality of routine examinations and RHC cannot meet the requirement for further analysis
- Severe loss of results of routine examinations (chest X-ray, electrocardiography, echocardiography, etc.)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality, 100037
Zhihong Liu, MD, PhD, 13269276067, zhihongliufuwai@163.com
About this study
Official title: Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension
Pulmonary hypertension represents a challenging and heterogeneous condition that is associated with high mortality and morbidity if left untreated. Artificial intelligence is used to study and develop theories and methods that simulate and extend human intelligence, which is being applied in fields related to cardiovascular diseases. The study intends to combine multimodal clinical data of patients who undergo right heart catheterization at Fuwai Hospital with artificial intelligence techniques to create programs that can screen and diagnose pulmonary hypertension.
Read the detailed description
Patients with pulmonary hypertension (PH) represent a challenging and heterogeneous cohort with high morbidity and mortality if left untreated. To make a definitive diagnosis of PH, one needs to conduct an invasive right heart catheterization (RHC) in order to assess the mean pulmonary artery pressure (mPAP). As PH occurs sporadically in various medical conditions, including connective tissue disease, and congenital heart disease, and presenting symptoms are non-specific, there is a need to raise the suspicion of PH early in the community. For this reason, noninvasive tools that are widely available for upfront screening would be ideal to enable timely diagnosis of PH. Transthoracic echocardiography has emerged as the mainstay for screening of PH, yet the sensitivity and specificity of this approach remain limited even in experienced hands. As high-throughput technologies advance and access to PH big data improve, it will be critical to prudently select artificial intelligence approaches for data analysis, visualization, and interpretation. By combining the multimodal clinical data (such as indicators from chest X-ray, electrocardiography, and echocardiography), this study aims to develop artificial intelligence-assisted programs to assist the screening and diagnosis of PH, and to evaluate its diagnostic accuracy for PH as compared with RHC, and to estimate whether this approach outperforms the conventional echocardiographic method.
- Being tested
- Right heart catheterization
- Keywords
- pulmonary hypertension, pulmonary vascular disease, right heart catheterization, echocardiography, electrocardiography, chest X-ray, artificial intelligence, machine learning, deep learning, screening, diagnosis
- Medical subject headings
- Hypertension, Pulmonary, Pulmonary Arterial Hypertension, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Right heart catheterizationDiagnostic test
RHC is commonly used essential test to make gold-standard diagnosis of PH with mPAP >20 mmHg. All multimodal data from patients eligible for inclusion would be randomly assigned to development datasets (70% of the study population) to train the artificial intelligence models for the detection of PH, which would be validated and tested by other datasets (30% of the study population).
| Study group | Role | What they receive |
|---|---|---|
| Patients with pulmonary hypertension | A series of routine examinations, including chest X-ray, electrocardiography, echocardiography, etc, would be performed on consecutive patients at Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. An RHC with an mPAP of >20 mmHg would confirm the diagnosis of PH. All these data will be collected as a source for machine learning or other artificial intelligence-assisted programs. Receives: Diagnostic Test: Right heart catheterization | |
| Patients without pulmonary hypertension | A series of routine examinations, including chest X-ray, electrocardiography, echocardiography, etc, would be performed on consecutive patients at Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. An RHC with an mPAP of ≤20 mmHg would confirm the absence of PH. All these data will be collected as a source for machine learning or other artificial intelligence-assisted programs. Receives: Diagnostic Test: Right heart catheterization |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of diagnosis by artificial intelligence-assisted algorithm
The investigators will calculate the area under the receiver operating characteristic curve of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.
Measured: Baseline
Secondary outcomes (2)
Sensitivity of diagnosis by artificial intelligence algorithm
The investigators will calculate the sensitivity of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.
Measured: Baseline
Specificity of diagnosis by artificial intelligence algorithm
The investigators will calculate the sensitivity of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Oct 2022
- Study start1 Jun 2022
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Pulmonary HypertensionStudies 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
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese Pulmonary Vascular Disease Research Group
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Zhihong Liu, MD, PhD, Principal investigator, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05566002
- Sponsor's protocol ID
- AIPHY Project
- Acronym
- AIPHY
- Status last verified
- Oct 2026
- First submitted
- 30 Sep 2022
- Last updated
- 22 Oct 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Huang Z, Diao X, Huo Y, Zhao Z, Geng J, Zhao Q, Liu J, Xi Q, Xia Y, Xu O, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z, Zhao W. Deep Learning-Enhanced Noninvasive Detection of Pulmonary Hypertension and Subtypes via Chest Radiographs, Validated by Catheterization. Chest. 2025 Nov;168(5):1215-1230. doi: 10.1016/j.chest.2025.06.008. Epub 2025 Jun 18.
PubMed 40541737Zhao W, Huang Z, Diao X, Yang Z, Zhao Z, Xia Y, Zhao Q, Sun Z, Xi Q, Huo Y, Xu O, Geng J, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z. Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension. NPJ Digit Med. 2025 Apr 10;8(1):198. doi: 10.1038/s41746-025-01593-3.
PubMed 40205021
Study at a glance
From the registry record, last updated by the sponsor on 22 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension
- ClinicalTrials.gov ID
- NCT05566002
- Acronym / sponsor ID
- AIPHY / AIPHY Project
- Keywords
- pulmonary hypertension, pulmonary vascular disease, right heart catheterization, echocardiography, electrocardiography, chest X-ray, artificial intelligence, machine learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Chinese Pulmonary Vascular Disease Research GroupUniversity, hospital or other
- Study start
- 1 Jun 2022 (actual)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 4 Oct 2022 (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.
Pulmonary hypertension represents a challenging and heterogeneous condition that is associated with high mortality and morbidity if left untreated. Artificial intelligence is used to study and develop theories and methods that simulate and extend human intelligence, which is being applied in fields related to cardiovascular diseases. The study intends to combine multimodal clinical data of patients who undergo right heart catheterization at Fuwai Hospital with artificial intelligence techniques to create programs that can screen and diagnose pulmonary hypertension.
Detailed description
Patients with pulmonary hypertension (PH) represent a challenging and heterogeneous cohort with high morbidity and mortality if left untreated. To make a definitive diagnosis of PH, one needs to conduct an invasive right heart catheterization (RHC) in order to assess the mean pulmonary artery pressure (mPAP). As PH occurs sporadically in various medical conditions, including connective tissue disease, and congenital heart disease, and presenting symptoms are non-specific, there is a need to raise the suspicion of PH early in the community. For this reason, noninvasive tools that are widely available for upfront screening would be ideal to enable timely diagnosis of PH. Transthoracic echocardiography has emerged as the mainstay for screening of PH, yet the sensitivity and specificity of this approach remain limited even in experienced hands. As high-throughput technologies advance and access to PH big data improve, it will be critical to prudently select artificial intelligence approaches for data analysis, visualization, and interpretation. By combining the multimodal clinical data (such as indicators from chest X-ray, electrocardiography, and echocardiography), this study aims to develop artificial intelligence-assisted programs to assist the screening and diagnosis of PH, and to evaluate its diagnostic accuracy for PH as compared with RHC, and to estimate whether this approach outperforms the conventional echocardiographic method.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with pulmonary hypertension | – | Diagnostic Test: Right heart catheterizationA series of routine examinations, including chest X-ray, electrocardiography, echocardiography, etc, would be performed on consecutive patients at Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. An RHC with an mPAP of >20 mmHg would confirm the diagnosis of PH. All these data will be collected as a source for machine learning or other artificial intelligence-assisted programs. |
| Patients without pulmonary hypertension | – | Diagnostic Test: Right heart catheterizationA series of routine examinations, including chest X-ray, electrocardiography, echocardiography, etc, would be performed on consecutive patients at Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. An RHC with an mPAP of ≤20 mmHg would confirm the absence of PH. All these data will be collected as a source for machine learning or other artificial intelligence-assisted programs. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients previously received chest X-rays, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China.
Key inclusion criteria (2 listed)
- Age ≥18 years old
- Patients previously received chest X-ray, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China
Key exclusion criteria (3 listed)
- Patients without RHC
- The quality of routine examinations and RHC cannot meet the requirement for further analysis
- Severe loss of results of routine examinations (chest X-ray, electrocardiography, echocardiography, etc.)
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 |
|---|---|---|
| Right heart catheterizationRHC is commonly used essential test to make gold-standard diagnosis of PH with mPAP >20 mmHg. All multimodal data from patients eligible for inclusion would be randomly assigned to development datasets (70% of the study population) to train the artificial intelligence models for the detection of PH, which would be validated and tested by other datasets (30% of the study population). | Diagnostic test | Patients with pulmonary hypertension, Patients without pulmonary hypertension |
Primary outcome
Accuracy of diagnosis by artificial intelligence-assisted algorithm
Time frame: Baseline
The investigators will calculate the area under the receiver operating characteristic curve of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.
2 secondary outcomes
Sensitivity of diagnosis by artificial intelligence algorithm
Time frame: Baseline
Specificity of diagnosis by artificial intelligence algorithm
Time frame: Baseline
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 22 Oct 2026.
- Derived from this study: PubMed 40541737Huang Z, Diao X, Huo Y, Zhao Z, Geng J, Zhao Q, Liu J, Xi Q, Xia Y, Xu O, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z, Zhao W. Deep Learning-Enhanced Noninvasive Detection of Pulmonary Hypertension and Subtypes via Chest Radiographs, Validated by Catheterization. Chest. 2025 Nov;168(5):1215-1230. doi: 10.1016/j.chest.2025.06.008. Epub 2025 Jun 18.
- Derived from this study: PubMed 40205021Zhao W, Huang Z, Diao X, Yang Z, Zhao Z, Xia Y, Zhao Q, Sun Z, Xi Q, Huo Y, Xu O, Geng J, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z. Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension. NPJ Digit Med. 2025 Apr 10;8(1):198. doi: 10.1038/s41746-025-01593-3.
- Individual participant dataSharing planned: Undecided
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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.
Zhihong Liu, MD, PhD principal investigator
Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Hypertension and Pulmonary Arterial Hypertension, run by Chinese Pulmonary Vascular Disease Research Group at 1 site in China.
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 3 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 22 Oct 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 China. 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 Zhihong Liu, MD, PhD (13269276067, zhihongliufuwai@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of diagnosis by artificial intelligence-assisted algorithm (time frame: Baseline). 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 NCT05566002, which also lists 2 linked publications.
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 22 Oct 2026.