Research Questions:
Can UK Biobank fundus image data be effectively used for the automatic typing of diabetic retinopathy (DR)?
Is it possible to construct an accurate DR risk prediction model based on these image features and clinical data?
What is the performance of the proposed model in predicting the onset and progression of DR?
Objectives:
To develop a deep learning model capable of analyzing fundus images from the UK Biobank database and automatically identifying and classifying diabetic retinopathy.
To integrate multi-modal data, including fundus image features, genetic information, lifestyle factors, and clinical indicators, to construct a comprehensive risk prediction model for diabetic retinopathy.
To validate and evaluate the accuracy and clinical utility of the constructed model for predicting the onset and progression of DR.
Scientific Rationale for the research:
Diabetic retinopathy is one of the leading causes of blindness worldwide. Early diagnosis and risk prediction are crucial for effective disease management. Traditional diagnostic methods rely on subjective assessments by ophthalmologists, which are often inefficient and subject to variability. The UK Biobank, with its vast and diverse dataset of fundus images, genetic and clinical data, provides a unique opportunity to develop and validate innovative, AI-based tools for DR diagnosis and risk prediction. This study aims to leverage this data to build an objective and efficient automated system. Such a system would improve early screening, risk stratification, and management of DR, thereby reducing the burden on healthcare systems and ultimately improving patient outcomes.