Automated Screening of Diabetic Retinopathy by Content

描述（由申请人提供）：我们的项目“根据内容自动筛查糖尿病视网膜病变”（R01 EY017065）的目标是研究使用基于内容的图像检索来检测并准确描述和索引人类视网膜疾病，特别是糖尿病视网膜病变的可行性，这些疾病是远程收集的低成本，非扩张的视网膜照片。基于内容的图像检索（CBIR）是基于图像内容从非常大的数据库集合中检索相关图像的过程。我们的概念假设预测，通过从数字图像中提取特征（内容信息），并将图像和相关元数据（上下文信息）与从大型编译的视网膜图像库中检索的类似、经过验证的图像进行比较，将出现基于计算机的（即自动化的）诊断能力。

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.