| Title: | The eye and the heart |
| Lead Institution: | Sun Yat-Sen University |
| Principal investigator: | Dr Wei Wang |
Application 62489
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About
Aims: The overall goal of this project is to develop and validate a screening test for predicting cardiovascular diseases (CVD) through eye imaging. Specific aims are: 1) To explore the associations between major eye diseases and a wide range of CVD. 2) To explore the associations between eye biomarkers, and CVD, as well as specific measures of cardiovascular structure. 3) To develop and validate the clinical application of deep learning to eye imaging for CVD, and cardiovascular structure prediction. Scientific rationale: Aging populations have inevitably led to a substantial rise in prevalence of age-related disease, including CVD. Accurate prediction of an individual risk of a disease is the foundation for targeting preventive treatment at persons who are asymptomatic but at high risk of the disease. However, currently available clinical prediction tools (e.g., Framingham risk score) are limited by the lack precision, involvement of invasive procedures, high costs and have other limitations. The eye shares striking structural, developmental, and physiological similarities with heart. Over the past decades, the research on this field has been developing rapidly and novel eye biomarkers have been identified to be associated with CVD. Nevertheless, much of the previous work has focused on a few features explicitly detected by ophthalmic experts using the standardized reviewing systems. Therefore, "personalized" image features and/or patterns have not yet fully investigated. Therefore, the ability of learning the "personalized" image features and/or patterns of the deep learning technology may improve the prediction of CVD based on retinal imaging. Duration of the project: 36 months. Public health impact: The heavy public burden of CVD calls for robust and feasible prediction models to identify high-risk individuals. Our proposed project, to develop and validate a screening test for predicting CVD incorporating deep learning techniques based on eye imaging, aims to obtain personalized systemic diseases' risk information. Thus our program has great potential to improve prediction precision. Furthermore, the low cost, easy accessibility and transfer, and non-invasive features make eye imaging suitable for large scale population screening. Thus, our program will help facilitate preventive strategies targeting at the right people and improve clinical outcomes, in turn reducing the burden of CVD.12 Publications
Enabling scientific discoveries that improve human health