Coronary Artery Disease
Conditions
Brief summary
The goal of this observational study are 1) to assess the effectiveness of modalities and/or their combination of multimodal non-contact information in predicting coronary artery disease; 2) to prospectively validate the performance of the developed artificial Intelligence models in predicting coronary artery disease.
Detailed description
This observational study aims to assess the effectiveness and potential mechanism of modalities of non-contact captured bio-physiological information, including facial RGB information, infrared thermography temperature information, gait information, and wearable device information, individually and/or in combination, in predicting coronary artery disease (CAD) with artificial intelligence technology. Individuals suspected of CAD and referred for evaluation will be invited to participate in the current study for analyzing the non-contact information and association with underlying CAD status, in order to establish the most efficient artificial Intelligence modeling strategy, and prospectively validate the predictive performance of the developed artificial Intelligence models for CAD prediction.
Interventions
OTHERNo intervention
No intervention
Sponsors
China National Center for Cardiovascular Diseases
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Suspected individuals referred to for coronary angiography or coronary computer tomography angiography.
Exclusion criteria
* Prior percutaneous coronary intervention (PCI) * Prior coronary artery bypass graft (CABG) * Undergoing confirmatory coronary evaluation as pre-operation routines for other cardiac diseases * With artificial body alteration (e.g. cosmetic surgery, facial trauma, or make-up) that may affect the non-contact information of study interest * Age less than 18 years old * Other circumstances that prevent participants from cooperating with the study process * Decline to consent for study participation
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of algorithm | At the end of enrollment (1 mouth) | Sensitivity of algorithm in predicting coronary artery disease assessed in test group |
| Specificity of algorithm | At the end of enrollment (1 mouth) | Sensitivity of algorithm in predicting coronary artery disease assessed in test group |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area under receiver operating curve (AUC) | At the end of enrollment (1 mouth) | Area under receiver operating curve of algorithm in predicting coronary artery disease assessed in test group |
Other
| Measure | Time frame | Description |
|---|---|---|
| Positive predictive value (PPV) of algorithm | At the end of enrollment (1 mouth) | Positive predictive value (PPV) of algorithm in predicting coronary artery disease assessed in test group |
| Negative predictive value (NPV) | At the end of enrollment (1 mouth) | Negative predictive value (NPV) of algorithm in predicting coronary artery disease assessed in test group |
| Diagnostic accuracy rate | At the end of enrollment (1 mouth) | Diagnostic accuracy rate of algorithm in predicting coronary artery disease assessed in test group |
Countries
China
Outcome results
None listed