The purposes of this study are 1) to explore the association between multi-dimension facial characteristics and the increased risk of coronary artery diseases (CAD); 2) to evaluate the diagnostic efficacy of multi-dimension appearance factors for coronary artery diseases.

Previous study demonstrated the feasibility of using deep learning to detect coronary artery disease based on facial photos. However, several limitations made the algorithm hard to be utilized in clinical practice, including low specificity and lack of external validation. Adding multi-dimension facial characteristics may further increase the algorithm effect.

Thus, the investigators designed a single-center, cross-sectional study to explore the association between multi-dimension facial characteristics and CAD and to evaluate the predictive efficacy of multi-dimension appearance factors for CAD. The investigators will recruit patients undergoing coronary angiography or coronary computer tomography angiography. Patients' baseline information and multi-dimension facial images will be collected. The investigators will train and validate a deep learning algorithm based on multi-dimension facial photos.

Inclusion Criteria:

• Undergoing coronary angiography or coronary computer tomography angiography
• Written informed consent

Exclusion Criteria:

• Prior percutaneous coronary intervention (PCI)
• Prior coronary artery bypass graft (CABG)
• Screening coronary artery disease before treating other heart diseases
• Without blood biochemistry outcome
• With artificially facial alteration (i.e. cosmetic surgery, facial trauma or make-up)
• Other situations which make patients fail to be photographed

Patients who undergo coronary angiography or coronary computer tomography angiography from both resident patients and outpatient.