Automated Retinal Analysis for Detection of Age Related Macular Degeneration

用于检测年龄相关性黄斑变性的自动视网膜分析

• 批准号: 8826350
• 负责人: Neil Michael Bressler
• 金额: $ 21.54万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8826350
• 关键词: Adult; Age related macular degeneration; Algorithms; Artificial Intelligence; Biotechnology; Blindness; Caring; Cataract; Classification; Clinical; Clinical Research; Clinical Treatment; Computer software; Computers; Data; Data Set; Detection; Development; Diabetic Retinopathy; Discipline; Drusen; Evaluation; Evolution; Eye; Eye diseases; Funding; Fundus; Generations; Goals; Grant; Image; Image Analysis; Individual; Institutes; Laboratories; Lead; Left; Life; Lighting; Machine Learning; Medical; Methods; Monitor; Morphologic artifacts; Myopia; National Eye Institute; Ophthalmologist; Outcome; Participant; Pathology; Patients; Performance; Physicians; Physics; Pigmentation physiologic function; Population; Principal Investigator; Procedures; Provider; Public Health; Research Personnel; Retina; Retinal; Risk; Scientist; Severities; Site; Software Tools; Staging; Structure of retinal pigment epithelium; Testing; Universities; Validation; Vision; Work; age related; bioimaging; computer program; database of Genotypes and Phenotypes; design; dietary supplements; experience; follow-up; geographic atrophy; improved; neovascular; novel; programs; public health relevance; response; screening; tool

 DESCRIPTION (provided by applicant): This study focuses on age-related macular degeneration (AMD) because, left untreated, it is the leading cause of blindness in the adult US population over 50. The goals of this program are twofold. First, the focus is to develop new screening tools to detect individuals with the intermediate stage of AMD, that are otherwise asymptomatic for vision loss, at a stage where they can be referred to a clinician for treatment and follow up, and when vision more likely can be preserved. The second goal is to develop Automated Retinal Image Analysis (ARIA) tools that help characterize Geographic Atrophy (GA), for which novel treatments are being investigated. We are developing data-driven algorithms for AMD screening which leverage recent advances in machine learning and machine intelligence and do not rely on detecting and counting or sizing drusen (a procedure which can be error prone). Instead, our method analyzes a fundus image as a whole using an image classification paradigm, and may also exploit ancillary patient information. Our goal is to use the AREDS dbGaP that includes thousands of images from hundreds of patients and offers a unique opportunity to develop and test these algorithms on a scale that has not previously been possible. Our team consists of the Johns Hopkins Wilmer Eye Institute, which will serve as the clinical analysis test site, and the Johns Hopkins University Applied Physics Laboratory, which will conduct the automated screening software tool analysis, development and testing.

 描述(申请人提供)：这项研究集中在老年性黄斑变性(AMD)，因为如果不进行治疗，它是美国50岁以上成年人口失明的主要原因。这个项目的目标是双重的。首先，重点是开发新的筛查工具，以检测患有AMD中期的人，这些人本来没有视力丧失的症状，在这个阶段，他们可以被转介到临床医生进行治疗和随访，当他们的视力更有可能得到保护的时候。第二个目标是开发自动化视网膜图像分析(ARIA)工具，帮助表征地理性萎缩(GA)，目前正在研究新的治疗方法。我们正在开发用于AMD筛查的数据驱动算法，该算法利用机器学习和机器智能的最新进展，不依赖于检测和计数或确定玻璃体大小(这是一个容易出错的过程)。相反，我们的方法使用图像分类范例将眼底图像作为一个整体进行分析，并可能利用辅助患者信息。我们的目标是使用AREDS DBGaP，它包括来自数百名患者的数千张图像，并提供了一个独特的机会，以前所未有的规模开发和测试这些算法。我们的团队由约翰霍普金斯大学威尔默眼科研究所和约翰霍普金斯大学应用物理实验室组成，约翰霍普金斯大学威尔默眼科研究所将作为临床分析测试地点，约翰霍普金斯大学应用物理实验室将进行自动化筛查软件工具的分析、开发和测试。