Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease
Trial Snapshot
- Phase
- Not Applicable
- Status
- Not yet recruiting
- Sponsor
- Tsinghua University
- Enrollment
- 1,570
- Primary Endpoint
- Accuracy
Study Overview
Brief 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 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:
Study Design
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Screening
- Masking
- Single (Outcomes Assessor)
Eligibility Criteria
- Ages
- 40 Years to 75 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
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
Arms & Interventions
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.
Intervention: AI-derived probability of coronary heart disease. (Diagnostic Test)
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.
Intervention: PCEs derived ASCVD risk (Diagnostic Test)
Outcomes
Primary Outcomes
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.
Secondary Outcomes
No secondary outcomes reported
Investigators
Tien Yin Wong
Professor
Tsinghua University