Advanced Image Analysis Tools for Diabetic Retinopathy Telemedicine Applications

摘要 在这个小型企业创新研究(SBIR)项目中，我们介绍了EyeArt，一种视网膜 用于糖尿病视网膜病变(DR)自动高诊断筛查的图像分析工具 新发现的功效。通过其与EyePACS的接口，这是一个免许可证、可扩展的远程医疗平台- Form，EyeArt将帮助扩大DR筛查并帮助弥合指数级 糖尿病患者的数量和眼部护理的数量之间的差距越来越大 供应商。 研究表明，拉丁裔人口总体上具有遗传易感性。 患上糖尿病。他们对糖尿病视网膜病变导致的视力丧失的易感性进一步 加上缺乏眼科临床医生，缺乏住院医生等因素， 保障，以及缺乏教育。根据卫生服务部(DHS)的说法 在洛杉矶县(LAC)，糖尿病患者的情况尤其严峻，目前 等待6-9个月以上的时间进行视网膜检查以筛查视网膜病变。 这可能会导致治疗延误并进展为不可逆转的视力丧失。至 帮助降低糖尿病人群的视力丧失风险，我们建议使用高级 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，允许重新确定眼科医生预约的优先顺序，并有助于分诊 高危患者。我们的第一阶段自动DR筛查工具原型已经 通过击败目前的学术和商业DR筛查AP显示出巨大的潜力 在大型公共视网膜数据集上的问题。展望未来，我们将在我们的方法基础上 并进一步开发创新的定制算法，用于关键的低层图像处理 步骤，同时利用计算机视觉和机器的最新进展， 高级学习领域，推理步骤，以产生临床级别的DR筛查 工具。我们的病变定位和筛查引擎将在功能上与 EyePACS将进一步推动筛查的扩大，特别是在以下方面受益- 资源筛查计划，如LAC-DHS安全网及其庞大的拉美裔美国人- 贝特人的人口。

Abstract 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 in- surance, 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 reprioritization 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 pro- cessing 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 dia- betic population.