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）：在此STTR项目中，我们介绍了Eyemark，这是一种使用视网膜图像照片自动计算糖尿病性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型神经瘤（MA）外观和消失率（共同称为离职率）的工具，以用作监测糖尿病性视网膜病（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医生护理的各个方面具有很大的积极影响，包括筛查，监测进展，药物发现和临床研究。有足够的公开证据表明，MA的周转率是进展到更严重的视网膜病的可能性的良好预测指标，从而确立了MA离职，成为糖尿病性视网膜病变的出色生物标志物。测量该数量涉及两个步骤：仔细对齐电流和基线图像，以及单个MAS的标记。如果完全由人类分级人士完成，这个过程非常耗时，容易出错。该项目的主要目标是通过自动化MA离职测量的步骤来克服上述限制：准确的图像注册和MA检测。我们将开发端端桌面软件，用于自动计算MA营业额，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性视网膜病变的进展。 公共卫生相关性：提出的工具将通过提供一种以非侵入性方式计算生物标志物的自动化工具，从而大大增强糖尿病性视网膜病患者可用的临床护理。这将使能够鉴定出更有可能发展为严重视​​网膜病的患者，从而有助于通过及时干预来防止此类患者的视力丧失。面对糖尿病患者的长期积压，早期识别尤其重要，并且可以通过早期识别来保存90％的视力丧失。有效的生物标志物的可用性还将通过促进潜在新疗法的生物学疗效的早期和可靠的确定来对药物发现过程产生积极影响。