Coronary Artery DiseaseNCT06092801
Prediction of Coronary Artery Disease Based on Multimodal, Non-contact Information With Artificial Intelligence
An observational study of Coronary Artery Disease, run by China National Center for Cardiovascular Diseases at 1 site in China.
Trial summary
The goal of this observational study are 1) to assess the effectiveness of modalities and/or their combination of multimodal non-contact information in predicting coronary artery disease; 2) to prospectively validate the performance of the developed artificial Intelligence models in predicting coronary artery disease. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Coronary Artery Disease
- How many people
- 2,978 participants
- Where
- 1 siteChina
- Study dates
- 20 Nov 2023to 9 Apr 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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: Individuals suspected of coronary artery disease and referred for confirmatory evaluation
Who may be able to join
- Suspected individuals referred to for coronary angiography or coronary computer tomography angiography.
Who cannot join
- Prior percutaneous coronary intervention (PCI)
- Prior coronary artery bypass graft (CABG)
- Undergoing confirmatory coronary evaluation as pre-operation routines for other cardiac diseases
- With artificial body alteration (e.g. cosmetic surgery, facial trauma, or make-up) that may affect the non-contact information of study interest
- Age less than 18 years old
- Other circumstances that prevent participants from cooperating with the study process
- Decline to consent for study participation
Trial locations and contacts
1 site in 1 country.
China1 site
Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality
About this study
Official title: Development and Validation of Artificial Intelligence Prediction Models Based on Multimodal, Non-contact Captured Information in Predicting Coronary Artery Disease
The goal of this observational study are 1) to assess the effectiveness of modalities and/or their combination of multimodal non-contact information in predicting coronary artery disease; 2) to prospectively validate the performance of the developed artificial Intelligence models in predicting coronary artery disease.
Read the detailed description
This observational study aims to assess the effectiveness and potential mechanism of modalities of non-contact captured bio-physiological information, including facial RGB information, infrared thermography temperature information, gait information, and wearable device information, individually and/or in combination, in predicting coronary artery disease (CAD) with artificial intelligence technology.
Individuals suspected of CAD and referred for evaluation will be invited to participate in the current study for analyzing the non-contact information and association with underlying CAD status, in order to establish the most efficient artificial Intelligence modeling strategy, and prospectively validate the predictive performance of the developed artificial Intelligence models for CAD prediction.
- Conditions
- Coronary Artery Disease
- Being tested
- No intervention
- Medical subject headings
- Coronary Artery Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention
| Study group | Role | What they receive |
|---|---|---|
| Individuals suspected of coronary artery disease | Individuals suspected of coronary artery disease and referred for evaluation Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of algorithm
Sensitivity of algorithm in predicting coronary artery disease assessed in test group
Measured: At the end of enrollment (1 mouth)
Specificity of algorithm
Sensitivity of algorithm in predicting coronary artery disease assessed in test group
Measured: At the end of enrollment (1 mouth)
Secondary outcomes (1)
Area under receiver operating curve (AUC)
Area under receiver operating curve of algorithm in predicting coronary artery disease assessed in test group
Measured: At the end of enrollment (1 mouth)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Oct 2023
- Study start20 Nov 2023
- Primary completion9 Apr 2025
- Study completion9 Apr 2025
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 Coronary Artery DiseaseStudies of the same condition, open and under way.
- More trials run by China National Center for Cardiovascular DiseasesThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 2,978 participants (actual)
Sponsor and investigators
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Shen Lin, M.D., Ph.D., Principal investigator, Chinese Academy of Medical Sciences, Fuwai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06092801
- Sponsor's protocol ID
- 2022-GSP-QN-10
- Status last verified
- Nov 2025
- First submitted
- 16 Oct 2023
- Last updated
- 28 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, Wang X, Lv B, Xu B, Song X, Zhang YJ, Cheng X, Huang W, Pu J, Zhang Q, Xia Y, Du B, Ji X, Zheng Z. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020 Dec 7;41(46):4400-4411. doi: 10.1093/eurheartj/ehaa640.
PubMed 32818267
Study at a glance
From the registry record, last updated by the sponsor on 28 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Artificial Intelligence Prediction Models Based on Multimodal, Non-contact Captured Information in Predicting Coronary Artery Disease
- ClinicalTrials.gov ID
- NCT06092801
- Acronym / sponsor ID
- 2022-GSP-QN-10
- Conditions
- Coronary Artery Disease
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,978 participants (actual)
- Lead sponsor
- China National Center for Cardiovascular DiseasesOther government
- Study start
- 20 Nov 2023 (actual)
- Primary completion
- 9 Apr 2025 (actual)
- Study completion
- 9 Apr 2025 (actual)
- First posted
- 23 Oct 2023 (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.
The goal of this observational study are 1) to assess the effectiveness of modalities and/or their combination of multimodal non-contact information in predicting coronary artery disease; 2) to prospectively validate the performance of the developed artificial Intelligence models in predicting coronary artery disease.
Detailed description
This observational study aims to assess the effectiveness and potential mechanism of modalities of non-contact captured bio-physiological information, including facial RGB information, infrared thermography temperature information, gait information, and wearable device information, individually and/or in combination, in predicting coronary artery disease (CAD) with artificial intelligence technology.
Individuals suspected of CAD and referred for evaluation will be invited to participate in the current study for analyzing the non-contact information and association with underlying CAD status, in order to establish the most efficient artificial Intelligence modeling strategy, and prospectively validate the predictive performance of the developed artificial Intelligence models for CAD prediction.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Individuals suspected of coronary artery disease | – | Other: No interventionIndividuals suspected of coronary artery disease and referred for evaluation |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Individuals suspected of coronary artery disease and referred for confirmatory evaluation
Key inclusion criteria (1 listed)
- Suspected individuals referred to for coronary angiography or coronary computer tomography angiography.
Key exclusion criteria (7 listed)
- Prior percutaneous coronary intervention (PCI)
- Prior coronary artery bypass graft (CABG)
- Undergoing confirmatory coronary evaluation as pre-operation routines for other cardiac diseases
- With artificial body alteration (e.g. cosmetic surgery, facial trauma, or make-up) that may affect the non-contact information of study interest
- Age less than 18 years old
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 |
|---|---|---|
| No interventionNo intervention | Other | Individuals suspected of coronary artery disease |
Primary outcome
Sensitivity of algorithm
Time frame: At the end of enrollment (1 mouth)
Sensitivity of algorithm in predicting coronary artery disease assessed in test group
Specificity of algorithm
Time frame: At the end of enrollment (1 mouth)
Sensitivity of algorithm in predicting coronary artery disease assessed in test group
1 secondary outcome
Area under receiver operating curve (AUC)
Time frame: At the end of enrollment (1 mouth)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Nov 2025.
- Background: PubMed 32818267Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, Wang X, Lv B, Xu B, Song X, Zhang YJ, Cheng X, Huang W, Pu J, Zhang Q, Xia Y, Du B, Ji X, Zheng Z. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020 Dec 7;41(46):4400-4411. doi: 10.1093/eurheartj/ehaa640.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Shen Lin, M.D., Ph.D. principal investigator
Chinese Academy of Medical Sciences, Fuwai Hospital
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 Coronary Artery Disease, run by China National Center for Cardiovascular Diseases 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 1 inclusion and 7 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: finished, as of the sponsor's last update on 28 Nov 2025. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of algorithm (time frame: At the end of enrollment (1 mouth)). The study lists 2 primary outcomes in all. 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 NCT06092801, 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 28 Nov 2025.