Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease (DeepCHD Plus)
Study Overview
Status
Conditions
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 Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: HONGWEI JI
- Phone Number: +8613120518791
- Email: hongweijicn@gmail.com
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion criteria:
- 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
Exclusion criteria:
- 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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: AI-Assisted Group (AI Group)
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.
|
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.
|
|
Active Comparator: Guideline-Based Group (Guideline Group)
Physicians use a PCE calculator to calculate the 10 year ASCVD risk.
This approach aligns with current clinical guidelines to assist in decision-making.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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.
|
Through study completion, an average of 1 week
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Tien Yin Wong, Tsinghua University
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimated)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- DeepCHD Plus
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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