Predicting Diabetic Retinopathy from Risk Factor Data and Digital Retinal Images

根据危险因素数据和数字视网膜图像预测糖尿病视网膜病变

• 批准号: 9353867
• 负责人: OMOLOLA I OGUNYEMI
• 金额: $ 47.99万
• 依托单位: CHARLES R. DREW UNIVERSITY OF MED & SCI
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2020-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9353867
• 关键词: Address; Adult; Affect; Age; Alaska Native; American Indians; Asian Americans; Blindness; Blood Circulation; Blood Vessels; Caring; Centers for Disease Control and Prevention (U.S.); Chronic; Clinic; Clinical; Complications of Diabetes Mellitus; Computer software; County; Data; Detection; Diabetes Mellitus; Diabetic Retinopathy; Diagnosis; Eye; Glucose; Health Care Reform; Health Insurance; Hispanics; Image; Incidence; International; Los Angeles; Machine Learning; Methods; Minority Groups; Modeling; Monitor; Not Hispanic or Latino; Online Systems; Operative Surgical Procedures; Ophthalmic examination and evaluation; Ophthalmologist; Optometrist; Patient risk; Patients; Pilot Projects; Population; Primary Health Care; Process; Protocols documentation; Publishing; Reader; Reading; Records; Reporting; Research Personnel; Resources; Retina; Retinal; Retinal Diseases; Risk; Risk Factors; Rural; Services; Software Tools; Specialist; Speed; Techniques; Telemedicine; Time; United States; Universities; Work; aged; base; diabetic; diabetic patient; digital; digital imaging; disorder prevention; ethnic minority population; high risk; image processing; inner city; laser photocoagulation; medical specialties; medically underserved; mortality; novel; outreach; predictive modeling; primary care setting; racial minority; randomized trial; safety net; screening; standard of care; statistics; tool; transmission process; trend

Abstract Diabetic retinopathy is the leading cause of blindness among US adults between the ages of 20 and 74 years. Laser photocoagulation surgery has been established as an effective way of treating retinopathy if it is detected early. Yearly retinal screening examinations are a potent tool in the battle to reduce the incidence of blindness from diabetic retinopathy because they provide diabetic patients with timely diagnoses and consequently, the potential for timely treatment. Primary care safety net clinics provide monitoring and other services for diabetic patients but they are often not equipped to provide specialty care services such as retinal screenings. Access to specialists who can provide retinal screenings can be increased through the use of telemedicine, which has shown great promise as a means of screening for diabetic retinopathy in the US and internationally. A pilot study by Charles Drew University investigators had a total of 2,876 teleretinal screenings performed for diabetic retinopathy, with 2,732 unique diabetic patients from six South Los Angeles safety net clinics screened. The present study aims to build on this prior work by: (a) developing novel software that utilizes information from clinical records to detect latent diabetic retinopathy in diabetic patients who have not yet received an annual eye examination, and (b) devising methods to speed up the diabetic retinopathy detection process for diabetic patients who have had digital retinal images taken by partially automating the process using image processing and machine learning techniques. Specifically, we propose to: 1. Develop predictive models for diabetic retinopathy using risk factors collected from patient clinical records. 2. Develop predictive models for automated diabetic retinopathy assessment using a combination of patient risk factor data and data from digital retinal images previously evaluated by experts. 3. Evaluate the predictive accuracy of: a) the models developed for specific aim 2, and, b) the assessments of optometrist readers against standard of care dilated retinal examinations by board certified ophthalmologists for 300 diabetic patients utilizing a new Los Angeles County reading center. 4. Create web-based software tools based on the predictive models developed in specific aim 1 that can be used to initiate outreach to high-risk patients in under-resourced settings, boosting detection rates for those patients who are most at risk for diabetic retinopathy. 5. Establish targeted outreach methods to promote screening for patients that the predictive models from specific aim 1 identify as potentially having undetected diabetic retinopathy.

抽象的 糖尿病视网膜病变是 20 至 74 岁美国成年人失明的主要原因。 激光光凝手术已被确定为治疗视网膜病变的有效方法，如果 及早发现。每年一次的视网膜筛查检查是​​减少视网膜病变发生率的有效工具 糖尿病视网膜病变导致的失明，因为它们为糖尿病患者提供了及时的诊断和治疗 因此，有可能得到及时治疗。初级保健安全网诊所提供监测和其他服务 为糖尿病患者提供服务，但他们往往没有能力提供视网膜等专科护理服务 放映。通过使用可以增加获得能够提供视网膜筛查的专家的机会 远程医疗在美国作为糖尿病视网膜病变筛查手段显示出巨大的前景 国际上。查尔斯德鲁大学研究人员进行的一项试点研究总共进行了 2,876 次远程视网膜筛查 对来自南洛杉矶六个安全网的 2,732 名独特糖尿病患者进行了糖尿病视网膜病变检查 诊所进行筛选。本研究旨在通过以下方式建立在先前工作的基础上：（a）开发新颖的软件 利用临床记录中的信息来检测未患有糖尿病的糖尿病患者的潜在糖尿病视网膜病变 尚未接受年度眼部检查，并且 (b) 设计加速糖尿病视网膜病变的方法 通过部分自动化拍摄数字视网膜图像的糖尿病患者的检测过程 使用图像处理和机器学习技术进行处理。具体来说，我们建议： 1. 使用从患者临床记录中收集的危险因素开发糖尿病视网膜病变的预测模型。 2. 结合患者的情况开发自动化糖尿病视网膜病变评估的预测模型 风险因素数据和来自专家先前评估的数字视网膜图像的数据。 3. 评估以下方面的预测准确性：a) 为特定目标 2 开发的模型，以及 b) 的评估 验光师读者反对经委员会认证的散瞳视网膜检查护理标准 眼科医生利用洛杉矶县新的阅读中心为 300 名糖尿病患者提供服务。 4. 根据特定目标 1 中开发的预测模型创建基于网络的软件工具，这些工具可以 用于向资源贫乏地区的高危患者开展外展活动，提高这些患者的检测率 最容易患糖尿病视网膜病变的患者。 5. 建立有针对性的推广方法，以促进对预测模型所依据的患者进行筛查 具体目标 1 确定可能患有未发现的糖尿病视网膜病变。