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Deep-Learning-Derived Endophenotypes from Retina Images

来自视网膜图像的深度学习衍生的内表型

基本信息

批准号：
10392211
负责人：
RUI CHEN
金额：
$ 58.65万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-30 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10392211
关键词：
3-Dimensional Adopted Age Algorithms Artificial Intelligence Candidate Disease Gene Classification Clinical Clinical Data Clinical Research Communities Computer software Data Descriptor Development Diabetes Mellitus Diabetic Retinopathy Diagnostic Disease Eye Eye diseases Funding Fundus Gender Genes Genetic Genetic study Goals Health Status Heritability Human Image Individual Knowledge Label Learning Liquid substance Manuals Maps Masks Medical Medical Imaging Methods Microaneurysm Modality Modeling Morphology Optical Coherence Tomography Output Participant Pathologic Patient imaging Patients Pattern Persons Phenotype Physiology Process Reading Reporting Research Retina Retinal Diseases Retinal Hemorrhage Scanning Self Perception Standardization Stimulus Supervision Testing Thick Training Validation Visual system structure Visualization base biobank bioinformatics tool cardiovascular risk factor clinical imaging data resource deep learning deep learning algorithm deep learning model disorder risk efficacy testing endophenotype fundus imaging genome wide association study genomic locus high dimensionality imaging genetics insight learning strategy macular edema multimodality natural language neovascularization neural network architecture novel novel strategies reconstruction response retinal imaging success supervised learning targeted treatment tool

项目摘要

Abstract The goal of this proposal is to establish a novel artificial intelligence-based strategy of genome wide association studies (GWAS) to identify new genetic loci associated with common human disorders. Identification of the genetic factors underlying these diseases will provide not only mechanistic insights into the diseases but also form the basis for developing novel preventative, diagnostic, and targeted therapeutic methods. While GWAS has achieved great successes in the past 1.5 decades, only a small portion of common diseases' heritability can be currently explained by loci identified from traditional GWAS. Leveraging on the public genetics and clinical imaging data, we propose a novel approach, termed image based GWAS (iGWAS), where GWAS will be performed on endophenotypes derived from images using cutting edge deep learning (DL) algorithms. By creating a more objective and quantitative output from the images, with less information loss, many new disease-associated genetic loci are expected to be identified. To test the efficacy of this novel approach, the human visual system will be used as an example. A DL-phenotyper, Multi-modal multi-view Self-Supervised deep Learning Encoder for Retinal images (MuSSLER), will be developed to extract quantitative output from optical coherence tomography scans and fundus images from both normal eyes and patients with diabetic retinopathy (DR). GWAS will be performed on normal eye endophenotypes to elucidate genes relevant to retina development and physiology. Also, GWAS will be performed on DR-specific endophenotypes to identify DR associated genetic loci and candidate genes. If successful, our approach represents a general framework that can be readily extended to the study of other retinal diseases and other common diseases with imaging data. Furthermore, the phenotyping neural network architecture established in this project can be readily adopted to develop an automated grading tool for heterogeneous clinical data and applied to many other common diseases.

摘要