Differential artery-vein analysis in OCT angiography for objective classification of diabetic retinopathy

OCT 血管造影中的动静脉差异分析用于糖尿病视网膜病变的客观分类

• 批准号: 10680158
• 负责人: Jennifer Irene Lim
• 金额: $ 25万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10680158
• 关键词: Administrative Supplement; African; African American student; Algorithms; American; Angiography; Animals; Arteries; Artificial Intelligence; Biomedical Engineering; Black race; Blood capillaries; Caring; Classification; Clinical; Clinical Management; Color; Consumption; Country; Detection; Diabetic Retinopathy; Environment; Evaluation; Eye; Eye diseases; Family; Fostering; Fundus; Fundus photography; Ghana; Health; Home; Human; Image; Mus; Nigeria; Ophthalmologist; Ophthalmology; Optical Coherence Tomography; Performance; Protocols documentation; Research; Resolution; Retina; Scanning; Speed; Standardization; Structure of central vein of the retina; Students; Technology; Telemedicine; Time; Training; Training and Education; Translating; Underrepresented Students; Validation; Veins; base; central retinal artery; comparative; computerized data processing; cost; deep learning; diabetic; disparity reduction; doctoral student; experience; graduate student; image reconstruction; imager; improved; interest; macular edema; portability; retinal imaging; rural area; student training; transfer learning; underserved area

Summary: This is an administrative supplement application for the R01 project, entitled ‘Differential artery-vein analysis in OCT angiography for objective classification of diabetic retinopathy’ (R01EY030842). The purpose of this supplement application is to add artificial intelligence (AI) topic research for enhanced retinal image construction and automated artery-vein analysis. Potential impact of the proposed AI topic research is twofold: 1) to advance AI technology in eye health; 2) to training graduate students interested in AI technology. One underrepresented American-born-Vietnamese student and two African/black students from underdeveloped countries will be involved in this project. It is known that diabetic retinopathy (DR) can target retinal arteries and veins differently. Therefore, differential artery-vein analysis can provide better performance of DR detection and classification. With this active R01 support, we have developed algorithms to achieve differential artery-vein analysis in OCTA for better clinical management of DR. For this supplement application, we propose to expand deep learning (DL) based AI approaches to foster clinical deployments of differential artery-vein analysis. The proposed AI research topics will naturally provide a useful platform to foster the education training of underrepresented students in biomedical engineering and AI ophthalmology. The first AI topic is transfer learning OCTA construction and DL artery-vein analysis. In traditional OCTA machine, multiple OCT image acquisitions are required, and subsequent correlation algorithms are employed. However, due to the requirement of multiple image acquisitions, there is a tradeoff between imaging speed and resolution/field-of-view in OCTA. We have recently demonstrated the feasibility of transfer learning OCTA construction from a single-volumetric- scan animal (mouse) OCT. We propose to validate DL based OCTA construction and artery-vein analysis using clinical OCT of human retina. The second AI topic is to validate a portable eye imager for high-fidelity artery- vein imaging and AI ophthalmology. Using clinical OCTA, we have demonstrated differential artery-vein analysis for improved DR detection. However, clinical OCTA machine is typically bulky and expensive, limiting their application for telemedicine in rural and underserved areas. Moreover, currently available clinical OCTA has a field of view (FOV) typically within 10-20o, corresponding to 3-6 mm retinal region. We propose to validate a portable eye imager with AI transfer learning for high-fidelity artery-vein analysis at capillary level. This portable, low-cost eye imager is based on our recently demonstrated high dynamic range (HDR) fundus camera and the AI transfer learning construction in AI topic 1 study. We anticipate that this portable eye imager will provide clinical OCTA level resolution for differential artery-vein analysis with a snapshot FOV up to 67o, corresponding to 20 mm retinal region. Successful validation of the portable, wide field, high-fidelity eye imager will provide an affordable solution to foster AI telemedicine at home and family care environments, and thus to reduce the disparity of eye care in rural and underserved areas.

摘要：这是R 01项目的行政补充申请，标题为“动脉-静脉差异” 糖尿病视网膜病变客观分类的OCT血管造影分析（R 01 EY 030842）。的目的 本补充申请是为增强视网膜图像添加人工智能（AI）主题研究 构建和自动化动脉-静脉分析。拟议的人工智能主题研究的潜在影响是 两方面：1）推进眼健康领域的AI技术; 2）培养对AI技术感兴趣的研究生。 一名代表性不足的美国出生的越南学生和两名来自不发达国家的非洲/黑人学生 各国将参与这一项目。已知糖尿病视网膜病变（DR）可以靶向视网膜动脉， 静脉不同。因此，差分动脉-静脉分析可以提供更好的DR检测性能， 分类.有了这种主动R 01支持，我们已经开发出算法，以实现差分动脉-静脉 OCTA分析，以更好地对DR进行临床管理。对于这种补充应用，我们建议扩展 基于深度学习（DL）的人工智能方法，以促进差分动脉-静脉分析的临床部署。的 拟议的人工智能研究课题自然会提供一个有用的平台，以促进教育培训， 生物医学工程和人工智能眼科学的学生人数不足。第一个AI话题是转移 学习OCTA构建和DL动静脉分析。在传统的OCTA机器中，多个OCT图像 需要进行采集，并采用随后的相关算法。然而，由于要求 在多次图像采集中，在OCTA中存在成像速度和分辨率/视场之间的权衡。 我们最近已经证明了从单体积迁移学习OCTA构建的可行性， 扫描动物（小鼠）OCT。我们建议使用 人类视网膜的临床OCT。第二个人工智能主题是验证便携式眼部成像仪的高保真动脉- 静脉成像和人工智能眼科。使用临床OCTA，我们已经证明了动脉-静脉分析的差异 用于改进DR检测。然而，临床OCTA机器通常体积庞大且昂贵，限制了它们的应用。 远程医疗在农村和服务不足地区的应用。此外，目前可用的临床OCTA具有 视场（FOV）通常在10- 20 °内，对应于3-6 mm的视网膜区域。我们建议验证一个 便携式眼部成像仪，具有AI迁移学习功能，可在毛细血管水平进行高保真动脉-静脉分析。这个便携式， 低成本的眼睛成像仪是基于我们最近展示的高动态范围（HDR）眼底相机和 AI主题1研究中的AI迁移学习构建。我们预计，这种便携式眼睛成像仪将提供 临床OCTA水平分辨率，用于差分动脉-静脉分析，快照FOV高达67 o， 到20 mm视网膜区域。便携式、宽视野、高保真眼部成像仪的成功验证将为 经济实惠的解决方案，以促进在家庭和家庭护理环境中的人工智能远程医疗，从而减少 农村和服务不足地区的眼科保健差距。