Coronary Heart DiseaseDeepCHDNCT06658600
Performance Evaluation of Artificial Intelligence Screening Model in Coronary Heart Disease Detection
A study testing Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease and Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease in people with Coronary Heart Disease, run by Tsinghua University at 3 sites in China.
Trial summary
To determine whether an integrated AI decision support can save time and improve accuracy of assessment of obstructive coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided measurements of obstructive CHD probability compared to clinical assessment in preliminary… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Coronary Heart Disease
- What is being tested?
- Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease and Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 900 participants
- Where
- 3 sitesChina
- Study dates
- 10 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 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Individuals with symptoms of coronary heart disease
- Age range: 18-75 years old
- Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials
Who cannot join
- Severe hypertension (>180/110mmHg)
- Complex arrhythmia (atrial fibrillation, atrial flutter, frequent premature beats)
- Severe lung disease and chest malformation or surgery patients
- Acute myocardial infarction occurring less than 3 months ago
- Individuals with severe liver and kidney dysfunction and electrolyte imbalance
Trial locations and contacts
3 sites in 1 country.
China3 sites
Tsinghua University
Beijing, Beijing Municipality, 100084
Shanghai Health and Medical Center
Shanghai, Shanghai Municipality, 200000
Shanghai Sixth People's Hospital
Shanghai, Shanghai Municipality, 200000
About this study
To determine whether an integrated AI decision support can save time and improve accuracy of assessment of obstructive coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided measurements of obstructive CHD probability compared to clinical assessment in preliminary evaluations by physicians.
Read the detailed description
This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in the preliminary assessment of obstructive CHD by physicians. Retrospectively collected medical records of participants with chest pain or dyspnea will be randomly assigned to either guideline group or AI group after baseline assessment:
There are three settings:
- Clinical Intuition (baseline assessment) Physicians assess obstructive CHD probability without any external assistance. Assessment relies solely on the physician's clinical judgment and experience.
- Guideline-Based Group (Guideline Group) Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD. This approach aligns with current clinical guidelines to assist in decision-making.
- AI-Assisted Group (AI Group) Physicians receive CHD probability estimates and diagnostic recommendations 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 obstructive CHD to a greater extent than standard clinical assessments, both compared to clinical intuition.
Secondary Objective To assess whether AI-guided decision support reduces the time required to complete preliminary assessments of obstructive CHD.
Participants, Readers and Randomization Participants: Case records of participants with chest pain or dyspnea, all underwent CT coronary angiography or invasive coronary angiography.
Readers: Physicians performing preliminary evaluations of obstructive CHD patients.
Randomization: Participants and readers will be randomized into one of the groups (RF-CL or AI) after clinical assessment at baseline using block randomization to ensure balanced group sizes.
- Conditions
- Coronary Heart Disease
- Being tested
- Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease, Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease
- Keywords
- obstructive coronary heart disease, retinal images, artificial intelligence
- Medical subject headings
- Coronary Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart diseaseOther
Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk.
Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart diseaseOther
Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD.
| Study group | Role | What they receive |
|---|---|---|
| Guideline-Based Group (Guideline Group) | Active comparator | Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD.
This approach aligns with current clinical guidelines to assist in decision-making. Receives: Other: Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease |
| AI-Assisted Group (AI Group) | Experimental | Physicians receive CHD probability estimates and diagnostic recommendations from an AI model based on retinal photographs.
The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk. Receives: Other: Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease
Whether AI-guided decision support improves the diagnostic accuracy of obstructive coronary heart disease (CHD) to a greater extent than standard clinical assessments (RF-CL), both compared to clinical intuition.
All participants of the case records had underwent CT angiography or invasive angiography. The diagnostic accuracy, sensitivity and specificity will be compared across groups.
Measured: Through study completion, an average of 1 week
Secondary outcomes (1)
Time Consumed by Physician Readers to Provide the Diagnosis Impression of Obstructive Coronary Heart Disease.
The time consumed by physician readers will be recorded by an algorithm implemented on the website for reading.
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 posted26 Oct 2024
- Study start10 Jan 2025
- 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 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tsinghua University
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Health and Medical Center
- Responsible party
- Principal investigator, Tien Yin Wong, Tsinghua University
- Investigators
- Tien Yin Wong, PhD, Principal investigator, Tsinghua University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06658600
- Sponsor's protocol ID
- DeepCHD
- Acronym
- DeepCHD
- Status last verified
- Apr 2025
- First submitted
- 22 Oct 2024
- Last updated
- 8 Apr 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Performance Evaluation of Artificial Intelligence Screening Model in Coronary Heart Disease Detection
- ClinicalTrials.gov ID
- NCT06658600
- Acronym / sponsor ID
- DeepCHD / DeepCHD
- Conditions
- Coronary Heart Disease
- Keywords
- obstructive coronary heart disease, retinal images, artificial intelligence
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Tsinghua UniversityUniversity, hospital or other
- Collaborators
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Health and Medical Center
- Study start
- 10 Jan 2025 (actual)
- Primary completion
- Apr 2025 (estimated)
- Study completion
- May 2025 (estimated)
- First posted
- 26 Oct 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 AI decision support can save time and improve accuracy of assessment of obstructive coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided measurements of obstructive CHD probability compared to clinical assessment in preliminary evaluations by physicians.
Detailed description
This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in the preliminary assessment of obstructive CHD by physicians. Retrospectively collected medical records of participants with chest pain or dyspnea will be randomly assigned to either guideline group or AI group after baseline assessment:
There are three settings:
- Clinical Intuition (baseline assessment) Physicians assess obstructive CHD probability without any external assistance. Assessment relies solely on the physician's clinical judgment and experience.
- Guideline-Based Group (Guideline Group) Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD. This approach aligns with current clinical guidelines to assist in decision-making.
- AI-Assisted Group (AI Group) Physicians receive CHD probability estimates and diagnostic recommendations 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 obstructive CHD to a greater extent than standard clinical assessments, both compared to clinical intuition.
Secondary Objective To assess whether AI-guided decision support reduces the time required to complete preliminary assessments of obstructive CHD.
Participants, Readers and Randomization Participants: Case records of participants with chest pain or dyspnea, all underwent CT coronary angiography or invasive coronary angiography.
Readers: Physicians performing preliminary evaluations of obstructive CHD patients.
Randomization: Participants and readers will be randomized into one of the groups (RF-CL 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 |
|---|---|---|
| Guideline-Based Group (Guideline Group) | Active comparator | Other: Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart diseasePhysicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD. This approach aligns with current clinical guidelines to assist in decision-making. |
| AI-Assisted Group (AI Group) | Experimental | Other: Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart diseasePhysicians receive CHD probability estimates and diagnostic recommendations from an AI model based on retinal photographs. The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Individuals with symptoms of coronary heart disease
- Age range: 18-75 years old
- Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials
Key exclusion criteria (5 listed)
- Severe hypertension (>180/110mmHg)
- Complex arrhythmia (atrial fibrillation, atrial flutter, frequent premature beats)
- Severe lung disease and chest malformation or surgery patients
- Acute myocardial infarction occurring less than 3 months ago
- 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 |
|---|---|---|
| Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart diseasePhysician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk. | Other | AI-Assisted Group (AI Group) |
| Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart diseasePhysicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD. | Other | Guideline-Based Group (Guideline Group) |
Primary outcome
Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease
Time frame: Through study completion, an average of 1 week
Whether AI-guided decision support improves the diagnostic accuracy of obstructive coronary heart disease (CHD) to a greater extent than standard clinical assessments (RF-CL), both compared to clinical intuition. All participants of the case records had underwent CT angiography or invasive angiography. The diagnostic accuracy, sensitivity and specificity will be compared across groups.
1 secondary outcome
Time Consumed by Physician Readers to Provide the Diagnosis Impression of Obstructive Coronary Heart Disease.
Time frame: Through study completion, an average of 1 week
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 8 Apr 2025.
- 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.
Tien Yin Wong, PhD principal investigator
Tsinghua University
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease and Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease in people with Coronary Heart Disease, run by Tsinghua University at 3 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 5 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: under way, not looking for participants, as of the sponsor's last update on 8 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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?
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?
Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease (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 8 Apr 2025.