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

• 批准号: 10706984
• 负责人: RUI CHEN
• 金额: $ 56.81万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 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/10706984
• 关键词: 3-Dimensional; Adopted; Age; Algorithms; Artificial Intelligence; Candidate Disease Gene; Chromosome Mapping; Classification; Clinical; Clinical Data; Clinical Research; Communities; Computer software; Data; Descriptor; Development; Diabetes Mellitus; Diabetic Retinopathy; Diagnostic; Disease; Exclusion; 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; Visualization; 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; 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

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.

抽象的 该提案的目标是建立一种新颖的基于人工智能的基因组策略 广泛关联研究（GWAS），以确定与普通人类相关的新遗传位点 失调。识别这些疾病背后的遗传因素不仅可以提供 对疾病的机制见解，也构成开发新型预防措施的基础， 诊断方法和靶向治疗方法。虽然 GWAS 在以下方面取得了巨大成功 过去1.5年，目前只有一小部分常见疾病的遗传力 通过传统 GWAS 确定的基因座进行解释。利用公共遗传学和 临床影像数据，我们提出了一种新方法，称为基于图像的 GWAS (i​​GWAS)， 将使用尖端技术对源自图像的内表型进行 GWAS 深度学习 (DL) 算法。通过创建更加客观和定量的输出 图像，信息丢失较少，许多新的与疾病相关的遗传位点有望 被识别。为了测试这种新颖方法的功效，将使用人类视觉系统 举个例子。 DL-表型，多模态多视图自监督深度学习 视网膜图像编码器（MuSSLER）将被开发来提取定量输出 正常眼睛和患者的光学相干断层扫描和眼底图像 患有糖尿病视网膜病变（DR）。将对正常眼睛内表型进行 GWAS，以 阐明与视网膜发育和生理学相关的基因。此外，还将进行 GWAS DR 特异性内表型，以确定 DR 相关基因位点和候选基因。如果 成功后，我们的方法代表了一个可以轻松扩展到的总体框架 利用影像数据研究其他视网膜疾病和其他常见疾病。此外， 该项目中建立的表型神经网络架构可以很容易地采用 开发针对异构临床数据的自动分级工具并应用于许多其他 常见疾病。