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: 10665804
• 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/10665804
• 关键词: Acceleration; Address; Adult; Age; Big Data; Blindness; Child; Chronic; Clinical Data; Clinical Management; Complex; Data; Data Science; Data Set; Decision Support Systems; Development; Dimensions; Disease; Disease Management; Disease Progression; Early Diagnosis; Eye; Eye diseases; Foundations; Genetic; Genetic Determinism; Genetic Markers; Genetic Research; Genetic study; Genomics; Genotype; Goals; Heterogeneity; Image; Image Analysis; 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; 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; progression risk; progressive neurodegeneration; rare variant; retinal imaging; risk variant; success; synergism; 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.

在美国，视力丧失是导致18岁以上成年人残疾的十大原因之一，也是导致残疾的最主要原因之一 儿童常见的致残情况。眼部的主要疾病是由视网膜慢性疾病引起的。 进行性神经变性和不幸的是不可逆转和不可治愈，因此早期诊断为 眼科疾病是临床医生提供视网膜保护的关键。眼科学的最新进展 成像和高通量基因分型和测序技术提供了令人兴奋的新机会 最终提高我们对眼部疾病、它们的遗传结构以及它们对 内表型和功能。然而，现有的遗传学和视网膜成像研究只进行了 另外，浪费了探索遗传学和视网膜图像之间相互作用的机会。因此， 迫切需要新的机器学习和科学进步来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了解疾病的进展。我们 建议开展新的视网膜成像遗传学研究，综合研究视网膜图像和 自动化眼病诊断和预后的遗传数据，遗传相关性研究 内表型和疾病进展预测。我们团队对视网膜进行了开创性的研究 遗传学、预测和图像分析，因此我们在实现这些目标方面处于独特的地位。 具体地说，我们将研究以下目标：1)构建高效的数据集成模型来集成 来自多个来源的视网膜成像遗传学数据；2)开发知识引导的学习模型 识别高维视网膜成像遗传学数据之间的非线性关联；3)检测 利用时间深度学习模型研究视网膜数据的纵向相关性；4)新的稳健公平度量 统一疾病预测和公平指标选择的学习模型；5)应用和验证 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。这个 这项提议的成功完成将产生尖端的机器学习工具，以促进 自动化疾病诊断和对疾病发展和进展的准确长期预测 这将加强对该疾病的早期预防和目前的临床管理，并将 为新的精准治疗发展提供见解。