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This study aims to develop a cardiovascular disease (CVD) screening tool and cardiovascular risk prediction tool based on fundus imaging data with the method of artificial intelligence.
Full description
This study will establish a cohort of individuals including patients with CVD and participants with high CVD risk, and all the study participants will be follow-up for 1 year. By collecting baseline clinical data, fundus imaging data, and CVD events during the follow up, this study aims to distinguish CVD status based on the fundus imaging data, and explore the association between fundus imaging data and occurence of CVD during the follow up. By using machine learning approach, this study aims to construct a CVD screening tool and CVD prediction tool based on fundus imaging data.
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Inclusion criteria
Three types of participants will be included, which are:
Participants with established coronary heart disease, including previously diagnosed myocardial infarction, previous treatment with coronary intervention or coronary artery bypass grafting, coronary artery stenosis ≥50%, or chest pain with objective evidence of myocardial ischemia (myocardial ischemia indicated by stress electrocardiogram or stress imaging)
Participants with established stroke.
Participants without coronary heart disease or stroke, but are at high risk for CVD, defined as meeting at least two of the following:
Exclusion criteria
Participants unable to provide fundus imaging data required for the study due to the following reasons:
Suffering from other serious diseases with an expected survival period of less than one year, such as advanced malignant tumors
Unable to adhere to follow-up
Other conditions which the researchers consider inappropriate for participants to enroll in the study
1,072 participants in 2 patient groups
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Central trial contact
Bin Wang, PhD, MD; Jing Li, PhD, MD
Data sourced from clinicaltrials.gov
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