Artificial Intelligence Algorithms, Coronary Artery Disease (CAD), Face, Screening
Conditions
Brief summary
This study aims to evaluate the effectiveness of this facial image-based AI algorithm for screening CAD in high-risk community populations (specifically individuals with diabetes, hypertension, or aged over 65). The main objectives are: 1. To verify if the AI algorithm can accurately distinguish between high-risk and low-risk groups by comparing the actual prevalence of CAD in these groups. 2. To compare the CAD detection rate using this AI screening strategy against the natural detection rate in a real-world cohort.
Interventions
OTHERNo Intervention
No Intervention
Sponsors
Zhoukou Specialized Disease Hospital
Zhoukou Chuanhui District Hospital of Traditional Chinese Medicine
Chinese Academy of Medical Sciences, Fuwai Hospital
Zhoukou Central Hospital
China National Center for Cardiovascular Diseases
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Age \>= 18 years. * Community high-risk population, defined as individuals meeting at least one of the following criteria: 1. Diagnosed with Diabetes Mellitus; 2. Diagnosed with Hypertension; 3. Advanced age (\> 65 years old).
Exclusion criteria
* Prior confirmed diagnosis of Coronary Heart Disease (CAD), including clinically diagnosed CAD, history of Coronary Artery Bypass Grafting (CABG), or history of Percutaneous Coronary Intervention (PCI). * History of diagnosed Heart Failure. * Significant facial alterations or conditions that may interfere with AI image analysis, such as plastic surgery, severe facial trauma, or heavy makeup. * Refusal to participate in the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Difference in Prevalence of CAD Between AI-Stratified High-Risk and Low-Risk Groups | Up to 6 months | Participants in the diagnostic test cohort are stratified into high-risk and low-risk groups using the AI screening strategy. Subsequently, all participants undergo Coronary Computed Tomography Angiography (CCTA). This outcome measures the difference in CAD prevalence between the two groups based on CCTA results. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of Major Adverse Cardiovascular Events (MACE) | 6 months | MACE is defined as a composite endpoint including all-cause death, myocardial infarction, and ischemia-driven coronary revascularization. The incidence of MACE will be compared between the diagnostic test cohort and the real-world observational cohort. |
| Incidence of All-Cause Death | 6 months | The number of participants who die from any cause during the follow-up period. |
| The detection rate of CAD | 6 months | The detection rate of CAD |
| Incidence of Ischemia-Driven Coronary Revascularization | 6 months | Defined as any percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) driven by ischemic symptoms or objective evidence of ischemia. |
| Incidence of Screening-Driven Coronary Revascularization | 6 months | Defined as coronary revascularization procedures (PCI or CABG) triggered by findings from screening examinations rather than acute clinical symptoms. |
| Incidence of Myocardial Infarction | 6 months | The number of participants who experience a myocardial infarction event, diagnosed based on standard clinical guidelines (e.g., symptoms, ECG changes, and troponin elevation). |
Countries
China
Contacts
Primary Contactzhe zheng, MD, PhD
Outcome results
None listed