Automated image-based biomarker computation tools for diabetic retinopathy

用于糖尿病视网膜病变的基于图像的自动化生物标志物计算工具

• 批准号: 8252674
• 负责人: Kaushal Solanki
• 金额: $ 26.09万
• 依托单位: EYENUK, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2013-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8252674
• 关键词: Address; Adoption; Algorithms; Appearance; Area; Biological; Biological Markers; Blindness; California; Caring; Classification; Clinic; Clinical; Clinical Research; Color; Computer Vision Systems; Computer software; Consultations; County; Descriptor; Detection; Development; Diabetic Retinopathy; Early identification; Eye; Face; Fundus; Future; Goals; Human; Image; Image Analysis; Imagery; Individual; Industry; Institutes; Intervention; Joints; Lesion; Longitudinal Studies; Los Angeles; Machine Learning; Manuals; Marketing; Measurement; Measures; Medical center; Methods; Microaneurysm; Monitor; Ophthalmic examination and evaluation; Optic Disk; Optometry; Patients; Pattern Recognition; Phase; Process; Publishing; Reading; Research; Retinal; Retinal Diseases; Screening procedure; Small Business Technology Transfer Research; Software Engineering; Software Tools; Time; Universities; Vision; Visit; base; clinical application; clinical care; clinical practice; clinically significant; cohort; design; diabetic patient; drug discovery; experience; image processing; image registration; macular edema; member; prevent; professor; success; tool; user-friendly

DESCRIPTION (provided by applicant): In this STTR project, we present EyeMark, a set of tools for automated computation of biomarkers for diabetic retinopathy using retinal image photographs. Specifically, we will develop tools for computation of microaneurysm (MA) appearance and disappearance rates (jointly known as turnover rates) for use as a biomarker in monitoring progression of diabetic retinopathy (DR). The availability of a reliable image-based biomarker will have high positive influence on various aspects of DR care, including screening, monitoring progression, drug discovery and clinical research. There is ample published evidence that MA turnover rates are a good predictor of likelihood of progression to more severe retinopathy, establishing MA turnover as an excellent biomarker for diabetic retinopathy. Measuring this quantity involves two steps: careful alignment of current and baseline images, and marking of individual MAs. This process is very time consuming and prone to error, if done by entirely by human graders. The primary goal of this project is to overcome the above limitations by automating both the steps involved in MA turnover measurement: accurate image registration, and MA detection. We will develop end-too-end desktop software for automated computation of MA turnover and also provide intuitive visualization tools for clinicians to more effectively monitor diabetic retinopathy progression. PUBLIC HEALTH RELEVANCE: The proposed tool will greatly enhance the clinical care available to diabetic retinopathy patients by providing an automated tool for computation of a biomarker in a non-invasive manner. This will enable identification of patients who are more likely to progress to severe retinopathy, thus helping prevent vision loss in such patients by timely intervention. Early identification is especially important in face of long backlog of diabetic patients waiting for an eye examination, and the fact that 90% of vision loss can be saved by early identification. The availability of an effective biomarker will also positively influence the drug discovery process by facilitating early and reliable determination of biological efficacy of potential new therapies.

描述（由申请人提供）：在这个STTR项目中，我们提出了EyeMark，一套使用视网膜图像照片自动计算糖尿病视网膜病变生物标志物的工具。具体而言，我们将开发用于计算微动脉瘤（MA）出现和消失率（统称为周转率）的工具，用作监测糖尿病视网膜病变（DR）进展的生物标志物。可靠的基于图像的生物标志物的可用性将对DR护理的各个方面产生高度积极的影响，包括筛查、监测进展、药物发现和临床研究。有大量已发表的证据表明，MA转换率是进展为更严重视网膜病变的可能性的良好预测因子，将MA转换确立为糖尿病视网膜病变的良好生物标志物。测量这个量涉及两个步骤：仔细对齐当前和基线图像，以及标记单个MA。这个过程非常耗时，如果完全由人类分级者完成，则容易出错。该项目的主要目标是通过自动化MA周转测量中涉及的两个步骤来克服上述限制：准确的图像配准和MA检测。我们将开发端对端桌面软件，用于自动计算MA周转率，并为临床医生提供直观的可视化工具，以更有效地监测糖尿病视网膜病变进展。 公共卫生相关性：所提出的工具将大大提高糖尿病视网膜病变患者的临床护理，通过提供一个自动化的工具，以非侵入性的方式计算的生物标志物。这将有助于识别更有可能进展为严重视网膜病变的患者，从而通过及时干预帮助预防此类患者的视力丧失。面对长期积压的糖尿病患者等待眼科检查，早期识别尤其重要，并且90%的视力丧失可以通过早期识别来挽救。有效生物标志物的可用性也将通过促进早期和可靠地确定潜在新疗法的生物学功效而对药物发现过程产生积极影响。