Assessing the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Artificial Intelligence to Assess the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases

Conditions
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Coronary Artery Disease

Intervention / Treatment
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* Other: No intervention


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Algorithm training and test group<br>Patients undergoing coronary angiography or coronary computer tomography angiography will be enrolled. Patients data will be used to training and validate the algorithm for CAD detection based on facial photos. | Other: No intervention<br>* No intervention<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area under receiver operating curve (AUC) | Area under receiver operating curve of algorithm assessed in test group | At the end of enrollment (1 mouth) | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity of algorithm | Sensitivity of algorithm assessed in test group | At the end of enrollment (1 mouth) | 
| Specificity of algorithm | Specificity of algorithm assessed in test group | At the end of enrollment (1 mouth) | 
| Positive predictive value (PPV) | PPV of algorithm assessed in test group | At the end of enrollment (1 mouth) | 
| Negative predictive value (NPV) | NPV of algorithm assessed in test group | At the end of enrollment (1 mouth) | 
| Diagnostic accuracy rate | Diagnostic accuracy rate of algorithm assessed in test group | At the end of enrollment (1 mouth) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial intelligence, Facial characteristics