Computer based screening for diabetic retinopathy

描述（由申请人提供）：这个提议的项目是由两个观察引起的。首先，如果没有视网膜图像的计算机辅助诊断，对糖尿病患者进行大规模视网膜病变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没有足够高的灵敏度或特异性。美国有超过2000万人患有糖尿病，据估计，其中不到一半的人定期接受糖尿病视网膜病变筛查。获得这种类型的医疗保健对个人来说是一个障碍，而为大量糖尿病患者提供负担得起的解决方案则是一个重大挑战。根据人类“读者”对每个病例进行评分，为美国公民建立一个全面的筛选程序，这将是非常昂贵的。像其他医学应用，如乳房x光检查和巴氏涂片检查一样，计算机辅助技术可以为我们的高危人群提供全面、定期筛查的解决方案。许多研究者已经开发了特定的算法，每一种算法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模态（像素格式，SLO，标准眼底镜，彩色，红色自由等）进行了测试。每台新相机都需要对算法进行重大调整。这个项目的目标是演示和验证一种全新的方法，用于计算机辅助视网膜图像分级。该算法基于人类视觉系统，并通过将每种算法的示例呈现给单个算法来“调谐”到每种类型的病变、形态、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。这项研究的意义是双重的。首先，通过提供一个有效的、健全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增加的效率。我们的系统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增加4-5倍的病例吞吐量。我们的产品开发计划扩展了我们方法的经济性。其次，通过提高阅读中心的工作效率，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。公共卫生相关性：目前约有1000万糖尿病患者未接受年度眼科检查。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。其结果是许多糖尿病患者早期丧失视力。为这一人群进行眼科检查的保健专家人数不足。如果没有基于计算机的筛查，就不可能对人口进行大规模筛查。

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.