Coronary Heart Disease (CHD)DeepCHD PlusNCT06695273
Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease
A study testing AI-derived probability of coronary heart disease. and PCEs derived ASCVD risk in people with Coronary Heart Disease (CHD), run by Tsinghua University.
Trial summary
To determine whether an integrated retinal AI decision support can improve predictive accuracy of coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided prediction of CHD compared to clinical prediction by physicians (e.g., usingPCEs), both using clinical intuition as… Read more about this study
- Who is it for?
- Ages 40 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Coronary Heart Disease (CHD)
- What is being tested?
- AI-derived probability of coronary heart disease. and PCEs derived ASCVD risk
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,570 participants
- Where
- No sites listed yet
- Study dates
- Jan 2025to May 2025
- Phase
- Interventional
- 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 40 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Individuals without uncontrolled vascular risk factors
- Age range: 40-75 years old
- Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials
Who cannot join
- Severe lung disease and cancer or surgery patients
- Statin user or pre-existing cardiovascular disease
- Individuals with severe liver and kidney dysfunction and electrolyte imbalance
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
To determine whether an integrated retinal AI decision support can improve predictive accuracy of coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided prediction of CHD compared to clinical prediction by physicians (e.g., usingPCEs), both using clinical intuition as baseline.
Read the detailed description
This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in CHD risk prediction and decision making by physicians. Prospective cohort study participant cases will be randomly assigned to either guideline group (e.g., PCEs) or AI group after baseline assessment (clinical intuition):
There are three settings: (1) Clinical Intuition (baseline assessment) Physicians' make decision about prevention strategy initiation (e.g., statin initiation) without any external assistance. Assessment relies solely on the physician's clinical judgment and experience. (2) Guideline-Based Group (Guideline Group) Physicians use a PCE table to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. (3) AI-Assisted Group (AI Group) Physicians receive CHD probability estimates from an AI model based on retinal photographs. The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk.
Primary Objective To evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the extent of prevention initiation (e.g., prescribing statins) corresponding with actual CHD outcomes observed.
Secondary Objective To assess whether AI-guided decision support reduces the time required to complete CHD assessments and decision making.
Participants, Readers and Randomization:
Participants: Participants in prospective cohort studies, with 10-year follow up.
Readers: Physicians performing evaluations of CHD probability and make primary prevention recommendations.
Randomization: Participants will be randomized into one of the groups (PCEs or AI) after clinical assessment at baseline using block randomization to ensure balanced group sizes.
- Conditions
- Coronary Heart Disease (CHD)
- Being tested
- AI-derived probability of coronary heart disease., PCEs derived ASCVD risk
- Medical subject headings
- Coronary Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-derived probability of coronary heart disease.Diagnostic test
Physician readers will be assisted with AI-derived probability of coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk.
PCEs derived ASCVD riskDiagnostic test
Physicians use a PCEs to calculate the probability of 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making.
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Group (AI Group) | Experimental | Physicians receive CHD probability estimates from an AI model based on retinal photographs. The AI tool provides individualized CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk. Receives: Diagnostic Test: AI-derived probability of coronary heart disease. |
| Guideline-Based Group (Guideline Group) | Active comparator | Physicians use a PCE calculator to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. Receives: Diagnostic Test: PCEs derived ASCVD risk |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
To evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the degree to which prevention initiation (e.g., prescribing statins) align with actual CHD outcomes observed.
Measured: Through study completion, an average of 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Nov 2024
- Study startJan 2025 (estimated)
- Primary completionApr 2025 (estimated)
- Study completionMay 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary Heart Disease (CHD)Studies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 1,570 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tsinghua University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Tien Yin Wong, Tsinghua University
- Investigators
- Tien Yin Wong, Principal investigator, Tsinghua University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06695273
- Sponsor's protocol ID
- DeepCHD Plus
- Acronym
- DeepCHD Plus
- Status last verified
- Nov 2024
- First submitted
- 16 Nov 2024
- Last updated
- 19 Nov 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease
- ClinicalTrials.gov ID
- NCT06695273
- Acronym / sponsor ID
- DeepCHD Plus / DeepCHD Plus
- Conditions
- Coronary Heart Disease (CHD)
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,570 participants (estimated)
- Lead sponsor
- Tsinghua UniversityUniversity, hospital or other
- Study start
- Jan 2025 (estimated)
- Primary completion
- Apr 2025 (estimated)
- Study completion
- May 2025 (estimated)
- First posted
- 19 Nov 2024 (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.
To determine whether an integrated retinal AI decision support can improve predictive accuracy of coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided prediction of CHD compared to clinical prediction by physicians (e.g., usingPCEs), both using clinical intuition as baseline.
Detailed description
This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in CHD risk prediction and decision making by physicians. Prospective cohort study participant cases will be randomly assigned to either guideline group (e.g., PCEs) or AI group after baseline assessment (clinical intuition):
There are three settings: (1) Clinical Intuition (baseline assessment) Physicians' make decision about prevention strategy initiation (e.g., statin initiation) without any external assistance. Assessment relies solely on the physician's clinical judgment and experience. (2) Guideline-Based Group (Guideline Group) Physicians use a PCE table to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. (3) AI-Assisted Group (AI Group) Physicians receive CHD probability estimates from an AI model based on retinal photographs. The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk.
Primary Objective To evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the extent of prevention initiation (e.g., prescribing statins) corresponding with actual CHD outcomes observed.
Secondary Objective To assess whether AI-guided decision support reduces the time required to complete CHD assessments and decision making.
Participants, Readers and Randomization:
Participants: Participants in prospective cohort studies, with 10-year follow up.
Readers: Physicians performing evaluations of CHD probability and make primary prevention recommendations.
Randomization: Participants will be randomized into one of the groups (PCEs or AI) after clinical assessment at baseline using block randomization to ensure balanced group sizes.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Group (AI Group) | Experimental | Diagnostic Test: AI-derived probability of coronary heart disease.Physicians receive CHD probability estimates from an AI model based on retinal photographs. The AI tool provides individualized CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk. |
| Guideline-Based Group (Guideline Group) | Active comparator | Diagnostic Test: PCEs derived ASCVD riskPhysicians use a PCE calculator to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. |
Who may qualify
- Ages
- 40 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Individuals without uncontrolled vascular risk factors
- Age range: 40-75 years old
- Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials
Key exclusion criteria (3 listed)
- Severe lung disease and cancer or surgery patients
- Statin user or pre-existing cardiovascular disease
- Individuals with severe liver and kidney dysfunction and electrolyte imbalance
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 |
|---|---|---|
| AI-derived probability of coronary heart disease.Physician readers will be assisted with AI-derived probability of coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk. | Diagnostic test | AI-Assisted Group (AI Group) |
| PCEs derived ASCVD riskPhysicians use a PCEs to calculate the probability of 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. | Diagnostic test | Guideline-Based Group (Guideline Group) |
Primary outcome
Accuracy
Time frame: Through study completion, an average of 1 week
To evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the degree to which prevention initiation (e.g., prescribing statins) align with actual CHD outcomes observed.
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 19 Nov 2024.
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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.
Tien Yin Wong principal investigator
Tsinghua University
Questions about this trial
What is this clinical trial studying?
A study testing AI-derived probability of coronary heart disease. and PCEs derived ASCVD risk in people with Coronary Heart Disease (CHD), run by Tsinghua University.
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: opening soon, as of the sponsor's last update on 19 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
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 HONGWEI JI (+8613120518791, hongweijicn@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: Through study completion, an average of 1 week). 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 19 Nov 2024.