Several age-related appearance factors were described associated with increased risk of coronary artery diseases (CAD). However, several limitations made these facial risk factors hard to be utilized in clinical practice, including 1) low prevalence in CAD patients, 2) lack of specific definition, 3) poor reproducibility in artificial recognition.~Thus, we designed a case-control study to explore new, common and well-defined facial characteristics associated with CAD and evaluate the diagnostic efficacy of appearence factors in CAD. We will recuit 1150 patients with at least one coronary lesion stenosis ≥50% according to coronary angiography or coronary computer tomography (CAD group) and 1150 patients without coronary lesion stenosis≥ 50% (No CAD group) judged by coronary angiography or coronary computer tomography. We will collect patients' baseline information and facial images. One hand, we will explore the facial factors associated with CAD by comparing the exposure rates of different facial factors in CAD group and No CAD group based on artificial intelligence technology. On the other hand, we will build a CAD risk model based on facial factors, and evaluate the diagnostic effect of the model.
The purpose of this study is to explore the facial characteristics associated with the increased risk of coronary artery diseases. The secondary purpose is to evaluate the diagnostic efficacy of appearance factors for coronary artery diseases.