Computer based screening for diabetic retinopathy

描述（由申请人提供）：这个拟议的项目是由两个观察的动机。首先，如果不引入视网膜图像的计算机辅助诊断，对糖尿病患者进行视网膜病变的大规模筛查在经济上是不可行的。其次，由家庭医生或其他非眼科医生进行的筛查不会产生足够高的灵敏度或特异性。 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筛查。获得这种类型的医疗保健对个人来说是一个障碍，而拥有一个负担得起的解决方案来为大量糖尿病患者提供这种服务则是一个重大挑战。基于人类“读者”对每个病例进行评分的美国公民全面筛查计划将耗资巨大。像其他医疗应用一样，如乳房X光检查和子宫颈抹片检查，计算机辅助技术可以为解决对我们的高危人群进行全面定期筛查的问题奠定基础。 许多研究者已经开发了特定的算法，每种算法都检测一种类型的病变，例如暗病变、白色病变或血管特征。这些算法已使用单一模态（像素格式、SLO、标准眼底镜、彩色、无红色等）进行了测试。每一个新的摄像机都需要对算法进行重大的重新调整。这个项目的目标是证明，然后验证一个全新的方法，计算机辅助分级的视网膜图像。该算法基于人类视觉系统，并通过将每种类型的病变、模态、分级系统等的示例呈现为单个算法而被“调谐”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将受益于我们的系统的效率增加。我们的系统不会取代当前的阅读器，它只是允许增加4-5倍的病例吞吐量，而不会牺牲灵敏度和特异性。我们的产品开发计划扩展了我们方法的经济性。第二，通过提高阅读中心的生产力，可以筛查更多的糖尿病高危人群，从而提高生活质量。公共卫生相关性：今天约有1000万糖尿病患者没有接受年度眼科检查。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。结果是许多糖尿病患者的视力早期丧失。 没有足够数量的医疗保健专家为这一人群进行眼科检查。如果没有计算机筛查，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.