Coronary Artery DiseaseVALUENCT06025305
Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography
An observational study of Coronary Artery Disease and Plaque, Atherosclerotic, run by Jinling Hospital, China at 1 site in China.
Trial summary
The goal of this observational study is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients. The main questions it aims to answer are: 1. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coronary Artery Disease, Plaque, Atherosclerotic
- What is being tested?
- Intravascular imaging test and Coronary plaque assessment
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2023to 31 Dec 2027
- 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
- No healthy volunteers
- Adult, Older adult
Study population: consecutive patients who first underwent CCTA and then Intravascular imaging in China
Who may be able to join
- Intravascular imaging (including intravascular ultrasound or optical coherence tomography) was performed within 3 months after CCTA;
- No change in medications or clinical symptoms during CCTA and intravascular imaging examinations;
- Coronary artery diameter stenosis of 30% to 90% on invasive coronary imaging.
Who cannot join
- Image quality of CCTA or intravascular US was inadequate to analyze;
- Intravascular imaging was performed after percutaneous coronary intervention (PCI) or pre-dilation of the target lesions;
- Lesions could not be co-registered between CCTA and intravascular US;
- Missing CCTA or intravascular US data
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
Research Institute Of Medical Imaging Jinling Hospital
Nanjing, Jiangsu, 210018
About this study
Official title: Development and Validation of Multi-scale Deep Neural Network-Based CT Intelligent Diagnosis System for Coronary Vulnerable Plaques: A Chinese Multicenter Study
The goal of this observational study is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients. The main questions it aims to answer are:
- Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;
- Whether the AI model enables to identify vulnerable plaques using intravascular ultrasound or optical coherence tomography as the reference standard.
- Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive CAD.
- Whether the AI model enables to influnece downstream clincial decision-making.
Read the detailed description
Coronary artery disease (CAD) remains the leading cause of death worldwide. Atherosclerotic plaques play a pivotal role in CAD-related patient mortality. Thus, the detection, quantification, and characterization of coronary plaques are clinically significant for early prevention and interventions for CAD.
Coronary CT angiography (CCTA) has emerged as a robust noninvasive tool for the evaluation of CAD. In clinical practice, the coronary plaque assessment is performed by a time-consuming manual process dependent on the clinician's experience and subjective visual interpretation. With the development of artificial intelligence, many automatic computer-aided methods have been proposed to post-process the CCTA images. However, previously proposed algorithms of plaque evaluation were not developed based on intravascular ultrasound (IVUS) or optical coherence tomography (OCT), which were regarded as the gold reference for plaque evaluation. Thus, we aimed to develop a deep learning model in a whole-process automatic and intelligent system on CCTA to detect, quantify, and characterize plaques using IVUS or OCT as reference standard. Then we will work on the validation in different clinical scenarios: (1) Validation of the accuracy of the new deep learning model; (2) Prognosis of the model in different populations with CAD; (3) Impact of the model on guiding clincial therapies.
The main questions it aims to answer are:
- Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;
- Whether the AI model enables to identify vulnerable plaques using IVUS or OCT as the reference standard.
- Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive coronary artery disease (China CT-FFR study 2).
- Whether the AI model enables to influnece downstream clincial decision-making in real-world clincial practice.
- Conditions
- Coronary Artery Disease, Plaque, Atherosclerotic
- Being tested
- Intravascular imaging test, Coronary plaque assessment
- Keywords
- artificial intelligence; coronary CT angiography; vulnerable plaque
- Medical subject headings
- Coronary Artery Disease, Plaque, Atherosclerotic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Intravascular imaging testDiagnostic test
Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months.
Coronary plaque assessmentDiagnostic test
Plaques on coronary CT angiography (CCTA) were quantified and characterized using the developed AI model.
| Study group | Role | What they receive |
|---|---|---|
| Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months | Not described Receives: Diagnostic Test: Intravascular imaging test | |
| Patients who underwent coronary CT angiography and optical coherence tomography within 3 months | Not described Receives: Diagnostic Test: Intravascular imaging test | |
| Patients who underwent coronary CT angiography because of suspected or known coronary artery disease | Not described Receives: Diagnostic Test: Coronary plaque assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging
Measured: 1 day
Secondary outcomes (3)
Overall coronary plaque detection rate using intravascular ultrasound as reference standard
Measured: 1 day
Total plaque volume
Measured: 1 day
Changes in medical management following the addition of the AI model compared with routine CCTA results alone.
Measured: 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Sep 2023
- Study start1 Jul 2023
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)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
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jinling Hospital, China
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhang longjiang,MD, Jinling Hospital, China
- Investigators
- Longjiang Zhang, MD, Study chair, Jinling Hospital, Medical School of Nanjing University, Nanjing,China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06025305
- Sponsor's protocol ID
- 2023DZKY-058-01
- Acronym
- VALUE
- Status last verified
- May 2026
- First submitted
- 30 Aug 2023
- Last updated
- 11 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Follmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vazquez Mezquita AJ, Biavati F, Isgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2024 Jan;21(1):51-64. doi: 10.1038/s41569-023-00900-3. Epub 2023 Jul 18.
PubMed 37464183Gaba P, Gersh BJ, Muller J, Narula J, Stone GW. Evolving concepts of the vulnerable atherosclerotic plaque and the vulnerable patient: implications for patient care and future research. Nat Rev Cardiol. 2023 Mar;20(3):181-196. doi: 10.1038/s41569-022-00769-8. Epub 2022 Sep 23.
PubMed 36151312Zhou F, Chen Q, Luo X, Cao W, Li Z, Zhang B, Schoepf UJ, Gill CE, Guo L, Gao H, Li Q, Shi Y, Tang T, Liu X, Wu H, Wang D, Xu F, Jin D, Huang S, Li H, Pan C, Gu H, Xie L, Wang X, Ye J, Jiang J, Zhao H, Fang X, Xu Y, Xing W, Li X, Yin X, Lu GM, Zhang LJ. Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study. Front Cardiovasc Med. 2022 Jan 31;8:778010. doi: 10.3389/fcvm.2021.778010. eCollection 2021.
PubMed 35174219Chen Q, Zhou F, Xing W, Xu Y, Hu S, Pan T, Cao W, Guo L, Shi Y, Luo S, Xu L, Zhang J, Zhang S, Zheng C, Yang Z, Qiao HY, Guo B, Liu T, Xu P, Xu W, Zhong J, Xie G, Tao X, Lu G, Tang CX, Zhang JJ, Zhang LJ; China VALUE Study Group. A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT Angiography. Radiology. 2026 Apr;319(1):e251967. doi: 10.1148/radiol.251967.
PubMed 42012347
Study at a glance
From the registry record, last updated by the sponsor on 11 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Multi-scale Deep Neural Network-Based CT Intelligent Diagnosis System for Coronary Vulnerable Plaques: A Chinese Multicenter Study
- ClinicalTrials.gov ID
- NCT06025305
- Acronym / sponsor ID
- VALUE / 2023DZKY-058-01
- Conditions
- Coronary Artery Disease, Plaque, Atherosclerotic
- Keywords
- artificial intelligence; coronary CT angiography; vulnerable plaque
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Jinling Hospital, ChinaUniversity, hospital or other
- Study start
- 1 Jul 2023 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 6 Sep 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 is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients. The main questions it aims to answer are:
- Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;
- Whether the AI model enables to identify vulnerable plaques using intravascular ultrasound or optical coherence tomography as the reference standard.
- Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive CAD.
- Whether the AI model enables to influnece downstream clincial decision-making.
Detailed description
Coronary artery disease (CAD) remains the leading cause of death worldwide. Atherosclerotic plaques play a pivotal role in CAD-related patient mortality. Thus, the detection, quantification, and characterization of coronary plaques are clinically significant for early prevention and interventions for CAD.
Coronary CT angiography (CCTA) has emerged as a robust noninvasive tool for the evaluation of CAD. In clinical practice, the coronary plaque assessment is performed by a time-consuming manual process dependent on the clinician's experience and subjective visual interpretation. With the development of artificial intelligence, many automatic computer-aided methods have been proposed to post-process the CCTA images. However, previously proposed algorithms of plaque evaluation were not developed based on intravascular ultrasound (IVUS) or optical coherence tomography (OCT), which were regarded as the gold reference for plaque evaluation. Thus, we aimed to develop a deep learning model in a whole-process automatic and intelligent system on CCTA to detect, quantify, and characterize plaques using IVUS or OCT as reference standard. Then we will work on the validation in different clinical scenarios: (1) Validation of the accuracy of the new deep learning model; (2) Prognosis of the model in different populations with CAD; (3) Impact of the model on guiding clincial therapies.
The main questions it aims to answer are:
- Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;
- Whether the AI model enables to identify vulnerable plaques using IVUS or OCT as the reference standard.
- Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive coronary artery disease (China CT-FFR study 2).
- Whether the AI model enables to influnece downstream clincial decision-making in real-world clincial practice.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months | – | Diagnostic Test: Intravascular imaging test |
| Patients who underwent coronary CT angiography and optical coherence tomography within 3 months | – | Diagnostic Test: Intravascular imaging test |
| Patients who underwent coronary CT angiography because of suspected or known coronary artery disease | – | Diagnostic Test: Coronary plaque assessment |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- consecutive patients who first underwent CCTA and then Intravascular imaging in China
Key inclusion criteria (3 listed)
- Intravascular imaging (including intravascular ultrasound or optical coherence tomography) was performed within 3 months after CCTA;
- No change in medications or clinical symptoms during CCTA and intravascular imaging examinations;
- Coronary artery diameter stenosis of 30% to 90% on invasive coronary imaging.
Key exclusion criteria (4 listed)
- Image quality of CCTA or intravascular US was inadequate to analyze;
- Intravascular imaging was performed after percutaneous coronary intervention (PCI) or pre-dilation of the target lesions;
- Lesions could not be co-registered between CCTA and intravascular US;
- Missing CCTA or intravascular US data
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 |
|---|---|---|
| Intravascular imaging testCoronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months. | Diagnostic test | Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months, Patients who underwent coronary CT angiography and optical coherence tomography within 3 months |
| Coronary plaque assessmentPlaques on coronary CT angiography (CCTA) were quantified and characterized using the developed AI model. | Diagnostic test | Patients who underwent coronary CT angiography because of suspected or known coronary artery disease |
Primary outcome
Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging
Time frame: 1 day
3 secondary outcomes
Overall coronary plaque detection rate using intravascular ultrasound as reference standard
Time frame: 1 day
Total plaque volume
Time frame: 1 day
Changes in medical management following the addition of the AI model compared with routine CCTA results alone.
Time frame: 90 days
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 11 May 2026.
- Background: PubMed 37464183Follmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vazquez Mezquita AJ, Biavati F, Isgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2024 Jan;21(1):51-64. doi: 10.1038/s41569-023-00900-3. Epub 2023 Jul 18.
- Background: PubMed 36151312Gaba P, Gersh BJ, Muller J, Narula J, Stone GW. Evolving concepts of the vulnerable atherosclerotic plaque and the vulnerable patient: implications for patient care and future research. Nat Rev Cardiol. 2023 Mar;20(3):181-196. doi: 10.1038/s41569-022-00769-8. Epub 2022 Sep 23.
- Results publication: PubMed 35174219Zhou F, Chen Q, Luo X, Cao W, Li Z, Zhang B, Schoepf UJ, Gill CE, Guo L, Gao H, Li Q, Shi Y, Tang T, Liu X, Wu H, Wang D, Xu F, Jin D, Huang S, Li H, Pan C, Gu H, Xie L, Wang X, Ye J, Jiang J, Zhao H, Fang X, Xu Y, Xing W, Li X, Yin X, Lu GM, Zhang LJ. Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study. Front Cardiovasc Med. 2022 Jan 31;8:778010. doi: 10.3389/fcvm.2021.778010. eCollection 2021.
- Derived from this study: PubMed 42012347Chen Q, Zhou F, Xing W, Xu Y, Hu S, Pan T, Cao W, Guo L, Shi Y, Luo S, Xu L, Zhang J, Zhang S, Zheng C, Yang Z, Qiao HY, Guo B, Liu T, Xu P, Xu W, Zhong J, Xie G, Tao X, Lu G, Tang CX, Zhang JJ, Zhang LJ; China VALUE Study Group. A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT Angiography. Radiology. 2026 Apr;319(1):e251967. doi: 10.1148/radiol.251967.
- 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.
Longjiang Zhang, MD study chair
Jinling Hospital, Medical School of Nanjing University, Nanjing,China
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 Plaque, Atherosclerotic, run by Jinling Hospital, China 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 3 inclusion and 4 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: enrolling by invitation, as of the sponsor's last update on 11 May 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?
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 and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging (time frame: 1 day). 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 NCT06025305, which also lists 4 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 11 May 2026.