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.
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
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
Parallel
AI-derived probability of coronary heart disease.
Diagnostic testPhysician 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 risk
Diagnostic testPhysicians use a PCEs to calculate the probability of 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making.
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.
Click on the lead sponsor to view all of their trials.
This trial is not recruiting at the moment. You can still explore other options:
