Automated Screening of Diabetic Retinopathy by Content

描述（由申请人提供）：我们的项目“通过内容自动筛查糖尿病视网膜病变”（R 01 EY 017065）的目标是研究使用基于内容的图像检索来检测和准确描述和索引人类视网膜疾病的可行性，特别是糖尿病视网膜病变，从低成本，非扩张的视网膜照片中远程收集。基于内容的图像检索（CBIR）是一种基于图像内容从大型数据库中检索相关图像的过程。我们的概念性假设预测，通过从数字图像（内容信息）中提取特征，并将图像和相关元数据（上下文信息）与从大型编译视网膜图像库中检索的类似验证图像进行比较，将出现基于计算机的（即自动化）诊断能力。 使用CBIR，我们已经成功地开发了一种基于网络的方法，允许远程诊断DR在初级保健健康设置，在真实的时间，通过远程访问基于计算机的，诊断，图像分析方法。我们在这次竞争性更新中提出的研究旨在通过我们目前的算法解决自动机器分割性能的关键方法。我们的目标是将性能提高到一个水平，这将允许实施作为一个完全自动化的病人护理模式与专家的能力，产生尽可能高的灵敏度和特异性的疾病检测。我们还将编译一个足够大的库，以验证我们的假设，即临床元数据（上下文数据）可以有助于CBIR方法的性能（灵敏度和特异性），为远程检测和诊断DR提供强大的诊断方法。DR治疗可用;我们的挑战在于找到一种成本有效的方法来检测和管理大量人群中的糖尿病眼病。基于计算机的成像应用于视网膜疾病的诊断，使用新的图像分析和临床元数据算法，有望实现低成本，自动化的诊断方法，以改善社区的眼睛健康，通过获得基于图像的“专家”诊断，为迅速扩大的高危人群中服务不足的患者。

DESCRIPTION (provided by applicant): The goal of our project "Automated Screening for Diabetic Retinopathy by Content" (R01 EY017065) is to investigate the feasibility of using content-based image retrieval to detect and accurately describe and index human retinal disease, specifically diabetic retinopathy, collected remotely from low-cost, non-dilated retinal photographs. Content-based image retrieval (CBIR) is the process of retrieving related images from very large database collections, based upon their pictorial content. Our conceptual hypothesis predicts that by extracting features from digital images (content information) and comparing the image and associated metadata (contextual information) to similar, validated images retrieved from a large compiled retinal image library, computer-based, (i.e. automated) diagnostic capabilities would emerge. Using CBIR, we have successfully developed a web-based method that permits remote diagnosis of DR in the primary care health setting, in real time, through remote access to a computer-based, diagnostic, image analysis method. The studies we propose in this competitive renewal are designed to address key methods in the performance of automated machine segmentation by our current algorithms. Our goal is to improve the performance to a level which will permit implementation as a fully automated patient care paradigm with expert capabilities that yield the highest possible sensitivity and specificity of disease detection. We will also compile a library large enough to validate our hypothesis that clinical metadata (contextual data) can contribute to the performance (sensitivity and specificity) of the CBIR method to provide a robust diagnostic method for remote detection and diagnosis of DR. PUBLIC HEALTH RELEVANCE: By 2030 it will be necessary to examine 1 million patients for diabetic eye disease every day worldwide. Treatment for DR is available; our challenge lies in finding a cost-effective approach to detecting and managing diabetic eye disease in large populations. The application of computer-based imaging to the diagnosis of retinal disease, using novel image analysis and clinical metadata algorithms hold the promise of achieving low-cost, automated, diagnostic methods to improve community eye health through access to image-based "expert" diagnosis for underserved patients in rapidly expanding at-risk populations.