Advanced Image Analysis Tools for Diabetic Retinopathy Telemedicine Applications

描述(申请人提供)：在这个小型企业创新研究(SBIR)项目中，我们介绍了EyeArt，这是一款用于糖尿病视网膜病变(DR)自动筛查的视网膜图像分析工具，具有很高的诊断效率。通过与EyePACS的接口，EyeArt将帮助扩大DR筛查的范围，并帮助弥合糖尿病患者数量和眼科护理提供者数量之间日益扩大的差距。EyePACS是一个免许可证、可扩展的远程医疗平台。研究表明，拉丁裔人口一般都有患糖尿病的遗传易感性。由于缺乏眼科临床医生、缺乏保险和缺乏教育等因素，他们更容易受到糖尿病视网膜病变造成的视力丧失的影响。根据洛杉矶县(LAC)卫生服务部(DHS)的数据，糖尿病患者的情况尤其严峻，目前视网膜病变筛查的视网膜检查等待时间超过6-9个月。这可能会导致治疗延误并进展为不可逆转的视力丧失。为了帮助降低这一糖尿病人群的视力丧失风险，我们建议将先进的图像分析算法与现有的远程医疗计划结合使用，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，并帮助对高危患者进行分诊。我们的第一阶段原型自动DR筛查工具已经显示出巨大的潜力，它在大型公共视网膜数据集上击败了现有的学术和商业DR筛查方法。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，以生产临床级别的DR筛查工具。我们的病变定位和筛查引擎将在功能上与EyePACS集成，以进一步推动筛查的扩展，特别是使资源不足的筛查项目受益，如LAC-DHS安全网及其庞大的西班牙裔糖尿病人口。

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.