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

SCH：新的先进机器学习框架，用于挖掘异构眼部数据以加速

• 批准号: 10601180
• 负责人: Wei Chen
• 金额: $ 30万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10601180
• 关键词: Address; Adult; Age; Big Data; Blindness; Child; Chronic; Clinical; Clinical Management; Complex; Data; Data Science; Data Set; Decision Support Systems; Development; Dimensions; Disease; Disease Management; Disease Progression; Early Diagnosis; Eye; Foundations; Genetic; Genetic Determinism; Genetic Markers; Genetic Research; Genetic study; Genomics; Genotype; Goals; Heterogeneity; Image; Image Analysis; Institutes; International; Intervention; Joints; Knowledge; Learning; Machine Learning; Mining; Modeling; Ophthalmologist; Ophthalmology; Outcome; Pathogenesis; Patient Monitoring; Patients; Play; Positioning Attribute; Precision therapeutics; Prevention; Reporting; Research; Retina; Risk; Risk Factors; Role; Scientific Advances and Accomplishments; Software Tools; Specialist; System; Systems Biology; Techniques; Time; Validation; Variant; base; case control; cohort; data complexity; data integration; data mining; deep learning model; design; disability; disease diagnosis; disease prognosis; endophenotype; genetic architecture; genetic association; genome wide association study; genome-wide; health data; heterogenous data; high dimensionality; high risk; imaging genetics; improved; innovation; insight; machine learning algorithm; machine learning framework; machine learning method; multiple data sources; novel; personalized health care; progressive neurodegeneration; rare variant; retinal imaging; risk variant; success; therapy development; tool; wasting

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.

视力丧失是美国成年人18岁以上的美国残疾的十大原因之一，也是最多的。 儿童常见的残疾条件。主要的眼部疾病是由视网膜慢性引起的 进行性神经变性，不幸的是不可逆转和无法治愈，因此早期诊断 眼疾病对于临床医生提供视网膜保护至关重要。眼科的最新进展 成像和高吞吐量基因分型和测序技术为令人兴奋的新机会提供了 最终提高了我们对眼部疾病，其遗传结构的理解及其对 内型和功能。但是，现有的遗传学和视网膜图像研究仅进行 另外，浪费了探索遗传学和视网膜图像之间的相互作用的机会。所以， 新的机器学习和科学进步迫切需要揭示视网膜的遗传基础 成像内表型，并协同遗传学和成像以理解疾病进展。我们 建议进行新型的视网膜成像遗传学研究，以整合视网膜图像和 自动眼疾病诊断和预后的遗传数据，遗传关联研究 内表型和疾病进展预测。我们的小组已经对视网膜进行了开创性研究 遗传学，预测和图像分析，因此我们处于实现这些目标的独特位置。 具体而言，我们将研究以下目的：1）建立有效的数据集成模型以集成 来自多个来源的视网膜成像遗传学数据； 2）开发知识指导的学习模型 确定高维视网膜成像遗传学数据之间的非线性关联； 3）检测 使用时间深度学习模型的视网膜数据中的纵向相互关系； 4）新的健壮公平度量 学习模型以统一疾病预测和公平的度量选择； 5）应用并验证提议 来自多个独立队列的大规模视网膜成像遗传学数据的机器学习方法。这 该提案的成功完成将产生尖端的机器学习工具以促进 自动疾病诊断和准确的疾病发展和进展的长期预测 轨迹将增强疾病的早期预防和当前的临床管理，并将 为新颖的精确治疗开发提供见解。