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: 7405554
• 负责人: Peter none Soliz
• 金额: $ 10.55万
• 依托单位: VISIONQUEST BIOMEDICAL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7405554
• 关键词: Algorithms; Aneurysm; Area; Background Diabetic Retinopathy; Blindness; Categories; Characteristics; Class; Classification; Clinic; Clinical; Color; Communities; Computer Assisted; Computer software; Computer-Assisted Diagnosis; Computers; Count; 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; Numbers; Ophthalmic examination and evaluation; Ophthalmologist; Ophthalmoscopes; Pap smear; Patients; Performance; Phase; Pilot Projects; Platelet Factor 4; Pliability; Population; Populations at Risk; Process; Productivity; Prospective Studies; Protocols documentation; Public Health; Published Comment; Purpose; Quality of life; ROC Curve; Rate; 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; Standards of Weights and Measures; System; Technology; Testing; Today; Training; Universities; Validation; Vision; Week; base; case control; cohort; cost; diabetic; digital; digital imaging; image processing; improved; independent component analysis; innovation; macular edema; neovascular; non-diabetic; novel strategies; programs

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（由申请人提供）：该提议的项目是由两个观察结果激励的。首先，对于视网膜病变的糖尿病患者的大规模筛查在经济上是过于刺激的，而没有引入视网膜图像的计算机辅助诊断。其次，由家庭医生或其他非恐怖分学家进行筛查不会导致足够高的灵敏度或特异性。 在美国，有超过2000万人患有糖尿病，据估计，不到一半的糖尿病会因糖尿病性视网膜病的定期筛查。获得这种类型的医疗保健是个人的障碍，同时拥有可负担的解决方案，可以为大量糖尿病患者提供这项服务带来重大挑战。根据人类“读者”为美国公民进行全面的筛查计划，以对每种情况进行评分，这非常昂贵。像其他医疗应用一样，乳房X线照片和子宫颈抹片检查，计算机辅助的技术可以为解决我们处于危险人群的全面，定期筛查的解决方案提供基础。 许多研究人员已经开发了特定的算法，每种算法都检测了一种类型的病变，例如黑暗病变，白色病变或血管特征。这些算法已经使用单个模态（像素格式，SLO，标准底尺，颜色，红色免费等）进行了测试。每个新相机都需要重新调整算法。该项目的目的是证明然后验证一种全新的视网膜图像评分分级的方法。该算法基于人类视觉系统，并通过将每个示例的示例呈现给单个算法“调整”到每种类型的病变，模态，分级系统等。将计算灵敏度和特异性。目标是达到99％的灵敏度和90％的特异性。 这项拟议的研究的意义是两倍。首先，通过提供经过验证的，可靠的计算机辅助分级系统，所有现有的阅读中心都将受益于我们系统的提高效率。我们的系统不能替代当前的读者，它仅允许通过案例4-5吞吐量的因素增加，而无需牺牲敏感性和特异性。我们的产品开发计划扩展了我们方法的经济学。其次，通过提高阅读中心的生产率，可以筛查更大的糖尿病患者人群，从而改善生活质量。公共卫生相关性：如今，大约有1000万个糖尿病患者未接受年度眼科检查。如果没有这些检查，就不可能尽早发现视网膜病变。结果是许多这些糖尿病患者早期失去视力。 对于该人群进行眼科检查的医疗专家数量不足。没有基于计算机的筛查，将无法对人口进行广泛的筛查。