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The research team has developed a deep learning algorithm that predicts anthropometric factors from fundus photographs and an algorithm that predicts cardiovascular disease risk. Fundus photographs are taken for various cardiovascular diseases (myocardial infarction, heart failure, hypertension with target organ damage, high-risk dyslipidemia, diabetic patients, and low-risk hypertension patients), and a deep learning algorithm for predicting developed anthropometric factors will be validated. Fundus photographs will also be taken twice in the first year, and additional fundus photographs will be taken two years later. Major cardiovascular events will be followed up for 5 years to verify the deep learning algorithm predicting cardiovascular disease risk prospectively.
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| Measure | Description | Time Frame |
|---|---|---|
| Major adverse cardiovascular disease | Composite of myocardial infarction, stroke, coronary revascularization including percutaneous coronary intervention and coronary bypass graft, and hospitalization for heart failure | 4 years |
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Visiting outpatient clinic in the tertiary hospital
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Sungha Park | Contact | +82-2228-8460 | shpark0530@yuhs.ac |
| Name | Affiliation | Role |
|---|---|---|
| Sungha Park | Severance Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yonsei University College of Medicine | Recruiting | Seoul | 03722 | South Korea |
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| ID | Term |
|---|---|
| D002318 | Cardiovascular Diseases |
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