Computer based screening for diabetic retinopathy

基于计算机的糖尿病视网膜病变筛查

• 批准号: 7869869
• 负责人: Peter none Soliz
• 金额: $ 5.87万
• 依托单位: VISIONQUEST BIOMEDICAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7869869
• 关键词: Algorithms; Aneurysm; Area; Background Diabetic Retinopathy; Blindness; Categories; Characteristics; Classification; Clinic; Clinical; Color; Communities; Computer Assisted; Computer software; Computer-Assisted Diagnosis; Computers; Data; Data Set; Development; Development Plans; Diabetes Mellitus; Diabetic Retinopathy; Diagnosis; Disease; Early Diagnosis; Economics; Elements; Ensure; Exudate; Eye; Family Physicians; Fluorescein Angiography; Foundations; Fundus; Goals; Healthcare; Human; Image; Individual; Institutes; Institution; International; Iowa; Lead; Lesion; Link; Macular degeneration; Mammography; Measures; Medical; Medical center; Methodology; Methods; Modality; Ophthalmic examination and evaluation; Ophthalmologist; Ophthalmoscopes; Pap smear; Patients; Performance; Phase; Pilot Projects; Platelet Factor 4; Population; Populations at Risk; Process; Productivity; Prospective Studies; Protocols documentation; Published Comment; Quality of life; ROC Curve; Reader; Reading; Research; Research Infrastructure; Research Personnel; Resolution; Resources; Retina; Retinal; Retinal Diseases; Risk; Sampling; Screening procedure; Sensitivity and Specificity; Services; Solutions; South Texas; Specialist; Specificity; Staging; System; Technology; Testing; Training; Universities; Validation; Vision; base; case control; cohort; cost; diabetic; diabetic patient; digital; digital imaging; flexibility; image processing; improved; independent component analysis; innovation; macular edema; meetings; neovascular; non-diabetic; novel strategies; product development; programs; public health relevance

DESCRIPTION (provided by applicant): This proposed project is motivated by two observations. First, broad-scale screening of diabetics for retinopathy is economically prohibitive without the introduction of computer-assisted diagnosis of retinal images. Second, screening by family physicians or other non-ophthalmologists does not result in sufficiently high sensitivity or specificity. There are over 20 million people in the US with diabetes and it is estimated that less than half of those are screened periodically for diabetic retinopathy. Access to this type of healthcare is an obstacle to the individual, while having an affordable solution to provide this service to the large volume of diabetics presents a significant challenge. Basing a comprehensive screening program for US citizens on human "readers" to grade each case would prohibitively expensive. Like other medical applications, such mammograms and Pap smears, computer-assisted technology could provide the foundation for the solution to comprehensive, periodic screening of our at risk population. Numerous investigators have developed specific algorithms, each to detect one type of lesion, such as dark lesions, white lesions, or vessel characteristics. These algorithms have been tested using a single modality (pixel format, SLO, standard funduscope, color, red free, etc). Each new camera requires significant re-tuning of the algorithms. The goal of this project is to demonstrate then validate an entirely new approach for computer-assisted grading of retinal images. This algorithm is based on the human vision system and is "tuned" to each type of lesion, modality, grading system, etc. through the presentation of examples of each to a single algorithm. Sensitivity and specificity will be calculated. The goal is to achieve 99% sensitivity and 90% specificity. The significance of this proposed research is two-fold. First, by providing a validated, robust computer-assisted grading system, all existing reading centers would benefit by the added efficiency of our system. Our system does not replace current readers, it simply allows increases by factors of 4-5 throughput of cases without sacrifice of sensitivity and specificity. Our Product Development Plan expands on the economics of our approach. Second, by increasing the productivity of reading centers, a much larger population of at risk diabetics can be screened, leading to improved quality of life. PUBLIC HEALTH RELEVANCE: Today there are about 10 million diabetics that are not receiving annual eye examinations. Without these examinations early detection of vision threatening retinopathy is not possible. The result is early loss of vision for many of these diabetics. There is an insufficient number of healthcare specialists to perform eye examinations for this population. Without the computer-based screening, a broad-scale screening of the population will not be possible.

描述(由申请者提供)：这个拟议的项目是由两个观察结果引起的。首先，在没有引入计算机辅助诊断视网膜图像的情况下，对糖尿病患者进行视网膜病变的大规模筛查在经济上是行不通的。其次，家庭医生或其他非眼科医生的筛查不会导致足够高的敏感性或特异性。美国有超过2000万糖尿病患者，据估计，其中不到一半的人定期接受糖尿病视网膜病变的筛查。获得这种类型的医疗保健对个人来说是一个障碍，而要有一个负担得起的解决方案来为大量的糖尿病患者提供这种服务则是一个巨大的挑战。根据人类的“阅读器”对每个病例进行分级，为美国公民建立一个全面的筛查程序，成本将高得令人望而却步。像其他医疗应用一样，如乳房X光检查和巴氏涂片，计算机辅助技术可以为解决我们的高危人群的全面、定期筛查提供基础。许多研究人员开发了特定的算法，每种算法都可以检测一种类型的病变，如深色病变、白色病变或血管特征。这些算法已经使用单一通道(像素格式、SLO、标准眼底镜、彩色、无红色等)进行了测试。每一台新相机都需要对算法进行重大重新调整。这个项目的目标是演示并验证一种全新的计算机辅助视网膜图像分级的方法。该算法基于人类视觉系统，并通过将每种类型的病变、形态、分级系统等的示例呈现为一个单独的算法来对其进行“调整”。将计算敏感度和特异度。目标是达到99%的敏感性和90%的特异性。这项拟议的研究的意义是双重的。首先，通过提供一个经过验证的、强大的计算机辅助评分系统，所有现有的阅读中心都将受益于我们系统的额外效率。我们的系统并不取代现有的阅读器，它只是允许在不牺牲敏感性和特异性的情况下将案件吞吐量增加4-5倍。我们的产品开发计划对我们方法的经济性进行了扩展。其次，通过提高阅读中心的生产率，可以对更多的糖尿病高危人群进行筛查，从而提高生活质量。与公共健康相关：今天，大约有1000万糖尿病患者没有接受年度眼睛检查。如果没有这些检查，早期发现威胁视力的视网膜病变是不可能的。其结果是这些糖尿病患者中的许多人过早地丧失了视力。医疗保健专家的数量不足，无法为这一人群进行眼睛检查。如果没有基于计算机的筛查，就不可能进行大规模的人口筛查。