Coronary Artery DiseaseCAD-AINCT07181512
Deep Learning CAD Screening on Chest CT
An observational study of Coronary Artery Disease and Coronary Artery Stenosis Stent, run by Yifan Guo at 2 sites in China.
Trial summary
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Many people have early atherosclerosis without symptoms, and some may develop significant coronary stenosis before any warning signs appear. 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, Coronary Artery Stenosis Stent
- What is being tested?
- Deep Learning Analysis of Non-contrast Chest CT
- How many people
- 200 participants
- Where
- 2 sitesChina
- Study dates
- 1 Sep 2025to 31 Dec 2027
- How long will I take part?
- 1 Year
- 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: Patients undergoing non-contrast chest CT and CCTA, either during outpatient or inpatient clinical care, or as part of high-risk health examinations. Both retrospective cases (2015-2024) and prospectively recruited patients (2025) are included from five medical centers in China.
Who may be able to join
- Age ≥18 years
- Patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days
- Coronary segments clearly visualized on non-contrast chest CT
Who cannot join
- Segments with motion artifacts, metal artifacts, or stents preventing analysis
- Vessel lumen completely obscured by calcification (unrecognizable vascular course)
- Inability to match coronary segment location between non-contrast chest CT and CCTA
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
The First Affiliated Hospital of Zhejiang Chinese Medical University
Hangzhou, Zhejiang, 310006
Maosheng Xu, MD, PhD, +86-571-87077272, xums166@zcmu.edu.cn
- Recruiting
The First Affiliated Hospital of Ningbo University
Ningbo, Zhejiang, 315000
Yuning Pan, MD, +86-574-87085093, fyypanyuning@nbu.edu.cn
About this study
Official title: Deep Learning-Based Opportunistic Screening of Coronary Artery Disease on Non-Contrast Chest CT: A Multicenter Study
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Many people have early atherosclerosis without symptoms, and some may develop significant coronary stenosis before any warning signs appear. Identifying high-risk individuals at an early stage is important to prevent heart attacks and other cardiovascular events.
Coronary CT angiography (CCTA) can directly evaluate plaque type and the degree of narrowing in the coronary arteries, but it is expensive, requires contrast injection, and involves higher radiation, making it unsuitable for large-scale screening. In contrast, non-contrast chest CT is widely used for health check-ups and lung disease follow-up. Such scans often provide clear views of certain coronary segments, which creates an opportunity to screen for CAD without additional cost or risk.
Read more
This multicenter study aims to develop and validate deep learning models to analyze coronary calcified segments that are visible on non-contrast chest CT. Two main objectives are: (1) to predict whether calcified segments contain mixed plaque components (both calcified and non-calcified); and (2) to predict whether these segments have significant narrowing (≥50% stenosis) as determined by CCTA. The study will also describe how often ≥50% stenosis is found in non-calcified segments, in order to demonstrate their low-risk nature.
The study includes retrospective data collected between 2015 and 2024, and a prospective external validation cohort starting in 2025. Approximately 1,417 patients with paired chest CT and CCTA have already been included for model development and testing. An additional 200 or more patients will be prospectively recruited for external validation.
This research may provide evidence that deep learning applied to routine non-contrast chest CT can serve as an opportunistic tool for early CAD risk screening in the general population.
Read the detailed description
This study involves analysis of imaging data obtained from patients who undergo non-contrast chest CT and CCTA as part of their routine clinical care. No additional imaging, radiation, or intervention is performed. The institutional review board approved the study and waived the requirement for written informed consent due to minimal risk and use of de-identified data.
- Being tested
- Deep Learning Analysis of Non-contrast Chest CT
- Keywords
- Coronary Artery Disease, Non-contrast Chest CT, Coronary CT Angiography, Opportunistic Screening, Artificial Intelligence
- 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.
Deep Learning Analysis of Non-contrast Chest CTOther
Analysis of clearly visualized coronary segments on non-contrast chest CT using deep learning models, compared with CCTA reference standard.
| Study group | Role | What they receive |
|---|---|---|
| Patients undergoing non-contrast chest CT and CCTA | A cohort of patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days. Clearly visualized coronary segments will be analyzed at the segment level for plaque composition and ≥50% stenosis using deep learning models. Both retrospective (2015-2024) and prospective (2025) cases are included. Receives: Other: Deep Learning Analysis of Non-contrast Chest CT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of plaque composition prediction
Discrimination ability of the deep learning model to classify calcified coronary segments as purely calcified or mixed plaque, using CCTA as the reference standard. Evaluated with AUC, sensitivity, specificity.
Measured: Baseline non-contrast chest CT to reference CCTA (within 30 days)
Accuracy of ≥50% stenosis prediction
Discrimination ability of the deep learning model to predict ≥50% luminal stenosis in calcified coronary segments, using CCTA as the reference standard. Evaluated with AUC, sensitivity, specificity, PPV, NPV.
Measured: Baseline non-contrast chest CT to reference CCTA (within 30 days)
Secondary outcomes (1)
Incidence of ≥50% stenosis in non-calcified segments
Descriptive statistics of ≥50% stenosis prevalence in non-calcified coronary artery segments.
Measured: Baseline non-contrast chest CT to CCTA (within 30 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Sep 2025
- Study start1 Sep 2025
- Primary completion20 Apr 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.
- 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
- Other
- Enrolment
- 200 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Yifan Guo
- Sponsor type
- Other government
- Collaborators
- Jinhua Municipal Central Hospital, The Second Affiliated Hospital of Fujian Medical University, First Affiliated Hospital of Ningbo University
- Responsible party
- Sponsor-investigator, Yifan Guo, Zhejiang Chinese Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07181512
- Sponsor's protocol ID
- CAD-AI-2025-V1.0
- Acronym
- CAD-AI
- Status last verified
- Feb 2026
- First submitted
- 11 Sep 2025
- Last updated
- 17 Feb 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: de-identified data available on reasonable request
Study at a glance
From the registry record, last updated by the sponsor on 17 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning-Based Opportunistic Screening of Coronary Artery Disease on Non-Contrast Chest CT: A Multicenter Study
- ClinicalTrials.gov ID
- NCT07181512
- Acronym / sponsor ID
- CAD-AI / CAD-AI-2025-V1.0
- Keywords
- Coronary Artery Disease, Non-contrast Chest CT, Coronary CT Angiography, Opportunistic Screening, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Yifan GuoOther government
- Collaborators
- Jinhua Municipal Central Hospital, The Second Affiliated Hospital of Fujian Medical University, First Affiliated Hospital of Ningbo University
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 20 Apr 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 18 Sep 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.
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Many people have early atherosclerosis without symptoms, and some may develop significant coronary stenosis before any warning signs appear. Identifying high-risk individuals at an early stage is important to prevent heart attacks and other cardiovascular events.
Coronary CT angiography (CCTA) can directly evaluate plaque type and the degree of narrowing in the coronary arteries, but it is expensive, requires contrast injection, and involves higher radiation, making it unsuitable for large-scale screening. In contrast, non-contrast chest CT is widely used for health check-ups and lung disease follow-up. Such scans often provide clear views of certain coronary segments, which creates an opportunity to screen for CAD without additional cost or risk.
This multicenter study aims to develop and validate deep learning models to analyze coronary calcified segments that are visible on non-contrast chest CT. Two main objectives are: (1) to predict whether calcified segments contain mixed plaque components (both calcified and non-calcified); and (2) to predict whether these segments have significant narrowing (≥50% stenosis) as determined by CCTA. The study will also describe how often ≥50% stenosis is found in non-calcified segments, in order to demonstrate their low-risk nature.
The study includes retrospective data collected between 2015 and 2024, and a prospective external validation cohort starting in 2025. Approximately 1,417 patients with paired chest CT and CCTA have already been included for model development and testing. An additional 200 or more patients will be prospectively recruited for external validation.
This research may provide evidence that deep learning applied to routine non-contrast chest CT can serve as an opportunistic tool for early CAD risk screening in the general population.
Detailed description
This study involves analysis of imaging data obtained from patients who undergo non-contrast chest CT and CCTA as part of their routine clinical care. No additional imaging, radiation, or intervention is performed. The institutional review board approved the study and waived the requirement for written informed consent due to minimal risk and use of de-identified data.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients undergoing non-contrast chest CT and CCTA | – | Other: Deep Learning Analysis of Non-contrast Chest CTA cohort of patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days. Clearly visualized coronary segments will be analyzed at the segment level for plaque composition and ≥50% stenosis using deep learning models. Both retrospective (2015-2024) and prospective (2025) cases are included. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing non-contrast chest CT and CCTA, either during outpatient or inpatient clinical care, or as part of high-risk health examinations. Both retrospective cases (2015-2024) and prospectively recruited patients (2025) are included from five medical centers in China.
Key inclusion criteria (3 listed)
- Age ≥18 years
- Patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days
- Coronary segments clearly visualized on non-contrast chest CT
Key exclusion criteria (3 listed)
- Segments with motion artifacts, metal artifacts, or stents preventing analysis
- Vessel lumen completely obscured by calcification (unrecognizable vascular course)
- Inability to match coronary segment location between non-contrast chest CT and CCTA
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 |
|---|---|---|
| Deep Learning Analysis of Non-contrast Chest CTAnalysis of clearly visualized coronary segments on non-contrast chest CT using deep learning models, compared with CCTA reference standard. | Other | Patients undergoing non-contrast chest CT and CCTA |
Primary outcome
Accuracy of plaque composition prediction
Time frame: Baseline non-contrast chest CT to reference CCTA (within 30 days)
Discrimination ability of the deep learning model to classify calcified coronary segments as purely calcified or mixed plaque, using CCTA as the reference standard. Evaluated with AUC, sensitivity, specificity.
Accuracy of ≥50% stenosis prediction
Time frame: Baseline non-contrast chest CT to reference CCTA (within 30 days)
Discrimination ability of the deep learning model to predict ≥50% luminal stenosis in calcified coronary segments, using CCTA as the reference standard. Evaluated with AUC, sensitivity, specificity, PPV, NPV.
1 secondary outcome
Incidence of ≥50% stenosis in non-calcified segments
Time frame: Baseline non-contrast chest CT to CCTA (within 30 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 17 Feb 2026.
- Individual participant dataSharing planned: No. de-identified data available on reasonable request
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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.
2 sites 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 Coronary Artery Stenosis Stent, run by Yifan Guo at 2 sites 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 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 17 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 2 sites 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 Yifan Guo, MD (+86-18072947783, 20193071@zcmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of plaque composition prediction (time frame: Baseline non-contrast chest CT to reference CCTA (within 30 days)). 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?
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 17 Feb 2026.