Ignite Creation Date:
2025-12-25 @ 2:03 AM
Ignite Modification Date:
2025-12-26 @ 2:28 AM
Study NCT ID:
NCT04941560
Status:
COMPLETED
Last Update Posted:
2023-03-21
First Post:
2021-06-22
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Assessing the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases
Sponsor:
China National Center for Cardiovascular Diseases
Organization:
Study Overview
Official Title:
Artificial Intelligence to Assess the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases
Status:
COMPLETED
Status Verified Date:
2023-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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.
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: