Advanced Image Analysis Tools for Diabetic Retinopathy Telemedicine Applications

描述（由申请人提供）：在这个小型企业创新研究（SBIR）项目中，我们提出了EyeArt，一种用于自动糖尿病视网膜病变（DR）筛查的视网膜图像分析工具，具有高诊断功效。通过与EyePACS（一种免许可、可扩展的远程医疗平台）的接口，EyeArt将有助于扩大DR筛查，并帮助弥合糖尿病患者数量与眼科护理提供者数量之间呈指数级增长的差距。研究表明，拉丁裔人口一般都有遗传倾向发展糖尿病。他们因糖尿病视网膜病变而视力丧失的脆弱性因缺乏眼科临床医生，缺乏保险和缺乏教育等因素而进一步加剧。根据洛杉矶县（LAC）的卫生服务部（DHS），糖尿病患者的情况特别严峻，目前等待视网膜检查以进行视网膜病变筛查的时间超过6-9个月。这可能导致治疗延迟和不可逆转的视力丧失。为了帮助降低糖尿病人群视力丧失的风险，我们建议使用先进的图像分析算法与现有的远程医疗计划相结合，以实现更快的筛查，允许眼科医生预约的重新优先化，并帮助高风险患者的分流。我们的第一阶段原型自动DR筛查工具已经通过在大型公共视网膜数据集上击败当前的学术和商业DR筛查方法显示出巨大的潜力。展望未来，我们将以我们的方法为基础，进一步开发创新的定制算法，用于关键的低级别图像处理步骤，同时利用计算机视觉和机器学习领域的最新进展，用于高级推理步骤，以产生临床级DR筛查工具。我们的病变定位和筛查引擎将在功能上与EyePACS集成，以进一步推动筛查的扩展，特别是有利于资源不足的筛查项目，如LAC-DHS安全网及其庞大的西班牙裔糖尿病人群。

DESCRIPTION (provided by applicant): In this small business innovations research (SBIR) project we present EyeArt, a retinal image analysis tool for automated diabetic retinopathy (DR) screenings with high diag- nostic efficacy. With its interface to EyePACS, a license-free, scalable telemedicine plat- form, EyeArt will aid the expansion of DR screening and help bridge the exponentially growing disparity between the number of diabetic patients and the number of eye-care providers. Research suggests that the Latino population in general are genetically predisposed to develop diabetes. Their vulnerability to vision loss due to diabetic retinopathy is further compounded by factors such as lack of access to ophthalmology clinicians, lack of insurance, and lack of education. According to the Department of Health Services (DHS) in Los Angeles County (LAC) the situation for diabetics is particularly grim, with current wait times upwards of 6-9 months for retinal examinations for retinopathy screening. This can lead to treatment delays and progression towards irreversible vision loss. To help reduce risk of vision loss in this diabetic population, we propose to use advanced image analysis algorithms in conjunction with existing telemedicine initiatives to enable faster screening, allow reprioritizatin of ophthalmologist appointments, and aid in triage of high-risk patients. Our phase I prototype automatic DR screening tool has already shown great potential by beating current academic and commercial DR screening ap- proaches on large public retinal datasets. Going forward, we will build on our approach and further develop innovative, customized algorithms for critical low-level image processing steps, while leveraging on recent advances in computer vision, and machine learning areas for high-level, inference steps to produce a clinical grade DR screening tool. Our lesion localization and screening engine will be functionally integrated with EyePACS to further drive the expansion of screening, particularly benefiting under- resourced screening programs like the LAC-DHS safety net and its large Hispanic diabetic population.