This study aims to evaluate the effectiveness of this facial image-based AI algorithm for screening CAD in high-risk community populations (specifically individuals with diabetes, hypertension, or aged over 65). The main objectives are:

1. To verify if the AI algorithm can accurately distinguish between high-risk and low-risk groups by comparing the actual prevalence of CAD in these groups.
2. To compare the CAD detection rate using this AI screening strategy against the natural detection rate in a real-world cohort.

Inclusion Criteria:

• Age >= 18 years.

• Community high-risk population, defined as individuals meeting at least one of the following criteria:

  1. Diagnosed with Diabetes Mellitus;
  2. Diagnosed with Hypertension;
  3. Advanced age (> 65 years old).

Exclusion Criteria:

• Prior confirmed diagnosis of Coronary Heart Disease (CAD), including clinically diagnosed CAD, history of Coronary Artery Bypass Grafting (CABG), or history of Percutaneous Coronary Intervention (PCI).
• History of diagnosed Heart Failure.
• Significant facial alterations or conditions that may interfere with AI image analysis, such as plastic surgery, severe facial trauma, or heavy makeup.
• Refusal to participate in the study.

The study population consists of community-dwelling residents identified as high-risk for Coronary Heart Disease (CHD). Participants are recruited from local community health centers and primary care settings. The cohort primarily comprises elderly individuals (aged > 65 years) and those with established cardiovascular risk factors, specifically diabetes mellitus and hypertension, who have not been previously diagnosed with CHD.