SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate

点击翻译按钮获取中文摘要

Vision loss is among the top 10 causes of disability in the U.S in adults over the age of 18 and one of the most common disabling conditions in children. The major ocular diseases are caused by the retinal chronic progressive neurodegeneration and unfortunately are irreversible and incurable, thus the early diagnosis of ocular diseases is crucial for clinician to provide retinoprotection. Recent advances in ophthalmological imaging and high throughput genotyping and sequencing techniques provide exciting new opportunities to ultimately improve our understanding of ocular diseases, their genetic architecture, and their influences on endophenotype and function. However, existing studies of genetics and retinal images are only conducted separately, wasting the opportunity to explore the interplay between genetics and retinal images. Therefore, there is a critical need for new machine learning and scientific advances to reveal genetic basis of retinal imaging endophenotypes and to synergize genetics and imaging for understanding disease progression. We propose to conduct the novel retinal imaging genetics research to integratively study both retinal images and genetic data for automated ocular disease diagnosis and prognosis, genetic association study of endophenotype, and disease progression prediction. Our group has performed pioneering research on retinal genetics, prediction, and image analysis, therefore we are in a unique position to achieve these goals. Specifically, we will investigate the following aims: 1) build efficient data integration models to integrate retinal imaging genetics data from multiple sources; 2) develop knowledge guided learning models for identifying nonlinear associations among high-dimensional retinal imaging genetics data; 3) detect the longitudinal interrelations in retinal data utilizing temporal deep learning model; 4) new robust fair metric learning model to unify the disease prediction and fair metric selection; 5) apply and validate the proposed machine learning methods to large-scale retinal imaging genetics data from multiple independent cohorts. The successful completion of this proposal will produce cutting-edge machine learning tools to facilitate automated disease diagnosis and accurate long-term prediction of disease development and progression trajectory, which will enhance the early prevention and current clinical management of the disease and will provide insights for novel precision treatment development.