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R01项目的行政补充申请，题为“差分动静脉” OCT 血管造影分析对糖尿病视网膜病变的客观分类”（R01EY030842）。 此补充应用程序是添加人工智能（AI）主题研究以增强视网膜图像 所提出的人工智能主题研究的潜在影响是。 双重目的：1）推进眼健康领域的人工智能技术；2）培养对人工智能技术感兴趣的研究生。 一名在美国出生的越南学生和两名来自欠发达地区的非洲/黑人学生人数不足 众所周知，糖尿病视网膜病变（DR）可以针对视网膜动脉和视网膜。 因此，差分动静脉分析可以提供更好的 DR 检测和性能。 借助这种主动 R01 支持，我们开发了算法来实现动静脉分类。 OCTA 分析以更好地进行 DR 临床管理 对于此补充应用程序，我们建议扩展该应用程序。 基于深度学习 (DL) 的人工智能方法可促进差分动静脉分析的临床部署。 提出的人工智能研究课题自然会为促进教育培训提供有用的平台 生物医学工程和人工智能眼科学生人数不足 第一个人工智能主题是转学。 学习 OCTA 构建和 DL 动静脉分析 在传统 OCTA 机器中，多个 OCT 图像。 然而，由于需要采集，因此采用了后续的相关算法。 对于多个图像采集，OCTA 中的成像速度和分辨率/视场之间存在权衡。 我们最近证明了从单体积的迁移学习 OCTA 构建的可行性 我们建议使用 DL 扫描动物（小鼠）OCTA 构建和动静脉分析。 第二个人工智能主题是验证用于高保真动脉的便携式眼部成像仪。 使用临床 OCTA，我们展示了静脉成像和 AI 眼科的差异动静脉分析。 然而，临床 OCTA 机器通常体积庞大且昂贵，限制了它们的应用。 此外，目前可用的临床 OCTA 还具有在农村和服务欠缺地区远程医疗的应用。 视场 (FOV) 通常在 10-20o 范围内，对应于 3-6 毫米的视网膜区域。 具有人工智能传输学习功能的便携式眼部成像仪，可在毛细血管水平进行高保真动静脉分析。 低成本眼部成像仪基于我们最近展示的高动态范围 (HDR) 眼底相机和 我们预计这款便携式眼部成像仪将提供人工智能主题 1 研究中的人工智能迁移学习构建。 用于差分动静脉分析的临床 OCTA 级别分辨率，快照 FOV 高达 67o，对应 便携式宽视场高保真眼部成像仪的成功验证将提供高达 20 毫米的视网膜区域。 经济实惠的解决方案，可在家庭和家庭护理环境中促进人工智能远程医疗，从而减少 农村和服务欠缺地区的眼保健差异。