| Title: | Machine learning for precision prediction of individual cardiovascular outcomes using cardiovascular structure and function domains |
| Lead Institution: | Seoul National University Hospital |
| Principal investigator: | Professor Seung-Pyo Lee |
Application 68330
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About
Conventional cardiovascular research uses various cardiovascular events ranging from death to myocardial infarction, heart failure, and arrhythmia as the endpoint of the study. However, the causes and risk factors that lead to each event are all different, and it is particularly important which factors have causal relationship and can be targeted for treatment. This study aimed to analyze cardiac magnetic resonance imaging (MRI) data, which provides detailed information on cardiovascular structure and function, along with large-scale individual data from the UK biobank using machine learning techniques. The current project will continue for 3 years. This study is expected to identify important parameters of cardiovascular structure (e.g., mass and size of heart chambers, or wall thickness) and function (e.g., stroke volume, ejection fraction, or cardiac output) that are important in precisely predicting individual cardiovascular outcomes. We are going to assess the causality between the parameters and diseases, which may reveal precise targets for treatment and prevention for cardiovascular diseases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14175 | Combined Effect of Air Pollution and Genetic Risk on Incident Cardiovascular Diseases | Tae-Min Rhee (+9) | 2024 | Journal of the American Heart Association |
Enabling scientific discoveries that improve human health