Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-derived probability of coronary heart disease., PCEs derived ASCVD risk.
- Who it may be relevant to
- Registry conditions: Coronary Heart Disease (CHD). Basic parameters: 40 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Diagnostic test 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 PCEs derived ASCVD risk
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.
Primary outcome measures
- Accuracy [Time frame: Through study completion, an average of 1 week]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Screening
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06695273 · DeepCHD Plus