Coronary Artery DiseaseNCT06102226
Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
An observational study of Coronary Artery Disease and Artificial Heart Device User, run by Yong Zeng at 1 site in China.
Trial summary
Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other… Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Coronary Artery Disease, Artificial Heart Device User
- What is being tested?
- coronary artery imaging (coronary CTA or coronary angiography)
- How many people
- 7,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2021to 30 Dec 2024
- 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 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography
Who may be able to join
- Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography.
Who cannot join
- The exclusion criteria were as follows: (i) prior percutaneous coronary intervention (PCI); (ii) prior coronary artery bypass graft (CABG); (iii) other heart disease (e.g., congenital heart disease, valvular heart disease, or macrovascular disease); (iv) inability to have photographs taken; and (v) and a diagnosis of ST-segment elevation myocardial infarction (STEMI). Prior to the coronary angiography procedure, all eligible patients provided informed consent to participate in the study and to have their photographs used for research purposes.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Yong Zeng
Beijing, Beijing Municipality, 100029
Yong Zeng, 18813085926, yzeng_anzhen@163.com
About this study
Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other systemic diseases. Artificial intelligence based on retinal fundus images can be used to detect anemia, hepatobiliary diseases, and chronic kidney disease, and to predict other systemic biomarkers. The above studies provide a theoretical basis for the application of artificial intelligence technology based on retinal fundus images to the diagnosis and prediction of cardiovascular diseases.
At present, there is still a lack of accurate, rapid, and easy-to-use diagnostic and therapeutic tools for predictive modeling of coronary heart disease risk and early screening tools in China and the world. Fundus image is gradually used as a tool for extensive screening of diseases due to its special connection with blood vessels throughout the body, as well as easy access, cheap and efficient. It is of great scientific and social significance to develop and validate a model for identification and prediction of coronary heart disease and its risk factors based on fundus images using AI deep learning algorithms, and to explore the value of AI fundus images in assisting coronary heart disease diagnosis and screening for a wide range of applications.
- Being tested
- coronary artery imaging (coronary CTA or coronary angiography)
- 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.
coronary artery imaging (coronary CTA or coronary angiography)Diagnostic test
In order to obtain the gold standard labeling for coronary heart disease, this topic will form a panel of experts on labeling, and the diagnosis will be based on coronary angiography, defined as a lesion with a stenosis of at least 50% in at least one coronary artery
| Study group | Role | What they receive |
|---|---|---|
| coronary artery disease group / non- coronary artery disease group | Recruited patients were categorized into a coronary artery disease group and a non-coronary artery disease group on the basis of coronary angiography findings, and the presence of CAD was defined as the presence of a coronary artery lesion with a stenosis Receives: Diagnostic Test: coronary artery imaging (coronary CTA or coronary angiography) |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC
To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated
Measured: December 30, 2024
Secondary outcomes (2)
sensitivity
To evaluate the algorithm performance, the sensitivity were calculated
Measured: December 30, 2024
specificity
To evaluate the algorithm performance, the specificity were calculated
Measured: December 30, 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Oct 2023
- Study start1 Jul 2021
- Primary completion1 Aug 2024 (estimated)
- Study completion30 Dec 2024 (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 Coronary Artery DiseaseStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 7,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yong Zeng
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Yong Zeng, Beijing Anzhen Hospital
- Investigators
- Yong Zeng, Principal investigator, Beijing An Zhen Hospital: Capital Medical University Affiliated Anzhen Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06102226
- Sponsor's protocol ID
- 121100004006885458
- Status last verified
- Oct 2023
- First submitted
- 22 Oct 2023
- Last updated
- 26 Oct 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ye Y, Feng W, Ding Y, Chen Q, Zhang Y, Lin L, Xia P, Ma T, Ju L, Wang B, Chang X, Wang X, Cai L, Ge Z, Zeng Y. Retinal image-based deep learning for mild cognitive impairment detection in coronary artery disease population. Heart. 2025 Oct 14;111(21):1013-1019. doi: 10.1136/heartjnl-2024-325486.
PubMed 40379470
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
- ClinicalTrials.gov ID
- NCT06102226
- Acronym / sponsor ID
- 121100004006885458
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 7,000 participants (estimated)
- Lead sponsor
- Yong ZengUniversity, hospital or other
- Study start
- 1 Jul 2021 (actual)
- Primary completion
- 1 Aug 2024 (estimated)
- Study completion
- 30 Dec 2024 (estimated)
- First posted
- 26 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.
Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other systemic diseases. Artificial intelligence based on retinal fundus images can be used to detect anemia, hepatobiliary diseases, and chronic kidney disease, and to predict other systemic biomarkers. The above studies provide a theoretical basis for the application of artificial intelligence technology based on retinal fundus images to the diagnosis and prediction of cardiovascular diseases.
At present, there is still a lack of accurate, rapid, and easy-to-use diagnostic and therapeutic tools for predictive modeling of coronary heart disease risk and early screening tools in China and the world. Fundus image is gradually used as a tool for extensive screening of diseases due to its special connection with blood vessels throughout the body, as well as easy access, cheap and efficient. It is of great scientific and social significance to develop and validate a model for identification and prediction of coronary heart disease and its risk factors based on fundus images using AI deep learning algorithms, and to explore the value of AI fundus images in assisting coronary heart disease diagnosis and screening for a wide range of applications.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| coronary artery disease group / non- coronary artery disease group | – | Diagnostic Test: coronary artery imaging (coronary CTA or coronary angiography)Recruited patients were categorized into a coronary artery disease group and a non-coronary artery disease group on the basis of coronary angiography findings, and the presence of CAD was defined as the presence of a coronary artery lesion with a stenosis |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography
Key inclusion criteria (1 listed)
- Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography.
Key exclusion criteria (1 listed)
- The exclusion criteria were as follows: (i) prior percutaneous coronary intervention (PCI); (ii) prior coronary artery bypass graft (CABG); (iii) other heart disease (e.g., congenital heart disease, valvular heart disease, or macrovascular disease); (iv) inability to have photographs taken; and (v) and a diagnosis of ST-segment elevation myocardial infarction (STEMI). Prior to the coronary angiography procedure, all eligible patients provided informed consent to participate in the study and to have their photographs used for research purposes.
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 |
|---|---|---|
| coronary artery imaging (coronary CTA or coronary angiography)In order to obtain the gold standard labeling for coronary heart disease, this topic will form a panel of experts on labeling, and the diagnosis will be based on coronary angiography, defined as a lesion with a stenosis of at least 50% in at least one coronary artery | Diagnostic test | coronary artery disease group / non- coronary artery disease group |
Primary outcome
AUC
Time frame: December 30, 2024
To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated
2 secondary outcomes
sensitivity
Time frame: December 30, 2024
specificity
Time frame: December 30, 2024
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Oct 2023.
- Derived from this study: PubMed 40379470Ye Y, Feng W, Ding Y, Chen Q, Zhang Y, Lin L, Xia P, Ma T, Ju L, Wang B, Chang X, Wang X, Cai L, Ge Z, Zeng Y. Retinal image-based deep learning for mild cognitive impairment detection in coronary artery disease population. Heart. 2025 Oct 14;111(21):1013-1019. doi: 10.1136/heartjnl-2024-325486.
- 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.
Yong Zeng principal investigator
Beijing An Zhen Hospital: Capital Medical University Affiliated Anzhen 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 and Artificial Heart Device User, run by Yong Zeng 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 1 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: status not confirmed, as of the sponsor's last update on 26 Oct 2023. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact yong zeng, Dr (+8613501373114, yzeng_anzhen@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC (time frame: December 30, 2024). 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 NCT06102226, 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 26 Oct 2023.