Clinical trial NCT04941560: Assessing the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases
Official title: Artificial Intelligence to Assess the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Coronary Artery Disease
Keywords: Artificial intelligence
Facial characteristics
Lead sponsor: China National Center for Cardiovascular Diseases
Sponsor class: OTHER_GOV
First posted: 2021-06-28
Enrollment: 460
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who undergo coronary angiography or coronary computer tomography angiography from both resident patients and outpatient.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Area under receiver operating curve (AUC)
timeFrame: At the end of enrollment (1 mouth)
Secondary outcomes:
measure: Sensitivity of algorithm
timeFrame: At the end of enrollment (1 mouth)
measure: Specificity of algorithm
timeFrame: At the end of enrollment (1 mouth)
measure: Positive predictive value (PPV)
timeFrame: At the end of enrollment (1 mouth)
measure: Negative predictive value (NPV)
timeFrame: At the end of enrollment (1 mouth)
measure: Diagnostic accuracy rate
timeFrame: At the end of enrollment (1 mouth)
Other outcomes:
Locations:
city: Beijing
country: China
facility: Fuwai hospital
state: Beijing
Study officials:
References: