Ocular Blood Flow Imaging for Glaucoma Assessment

用于青光眼评估的眼血流成像

• 批准号: 10867973
• 负责人: Abhishek Rege
• 金额: $ 24.58万
• 依托单位: VASOPTIC MEDICAL, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-03 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10867973
• 关键词: Administrative Supplement; Adult; Blindness; Blood flow; Clinic; Collection; Computer Vision Systems; Data; Data Analyses; Data Set; Diagnostic; Early Diagnosis; Eye; Future; Glaucoma; Goals; Hyperoxia; Image; Machine Learning; Myopia; Parents; Patients; Phase; Public Health; Refractive Errors; Research; Small Business Innovation Research Grant; Standardization; Techniques; Technology; Time; United States; biomarker identification; health goals; human subject; instrument; multimodality; parent grant; software development

ABSTRACT Our parent Phase II SBIR project seeks to develop effective diagnostic solutions for glaucoma, which is one of the leading causes of adult blindness in the United States and globally. Specific tasks undertaken are the collection of ocular blood flow data using our proprietary XyCAM technology from 150 human subjects in the glaucoma clinic at baseline and following hyperoxia, and the subsequent analysis of acquired data to identify biomarkers for early diagnosis of glaucoma. In particular, the plan is to evaluate the suitability of our approach in up to 30 subjects with a high degree of myopia (refractive error of more than 6 Diopters). We are requesting this administrative supplement to the parent grant to overcome certain data analysis challenges that have become apparent since we started the project. Notable among these is the need for registration of data acquired by the XyCAM to the data acquired by other instruments, and also to the data acquired by the XyCAM at other time points (such as post-hyperoxia) so that data analysis can be conducted at a regional level in a reliable and standardized manner. We propose to develop certain automated techniques by utilizing computer vision and machine learning approaches to support analyses of large volumes of data as will be collected during this study. In addition to these proposed activities facilitating project goals in the short term, we also anticipate that the developed software will prove useful more broadly in the future for multi-modal or longitudinal comparison of XyCAM-generated ocular blood flow data.

摘要 我们的母公司二期SBIR项目旨在开发有效的青光眼诊断解决方案， 这是美国和全球成人失明的主要原因之一。具体 所承担的任务是使用我们专有的XyCAM收集眼部血流数据 技术从青光眼诊所的150名人类受试者在基线和高氧后， 以及随后分析所获得的数据以鉴定用于早期诊断的生物标志物， 青光眼特别是，该计划将评估我们的方法在多达30个科目中的适用性 高度近视（屈光不正超过6度）。我们要求 父母补助金的行政补充，以克服某些数据分析挑战， 自从我们开始这个项目以来就变得明显了。其中值得注意的是， 将XyCAM采集的数据与其他仪器采集的数据配准， XyCAM在其他时间点（如高氧后）采集的数据， 可以在区域一级以可靠和标准化的方式进行分析。我们提出 通过利用计算机视觉和机器学习来开发某些自动化技术 支持对本研究期间将收集的大量数据进行分析的方法。在 除了这些在短期内促进项目目标的拟议活动外，我们还预计， 开发的软件将在未来更广泛地用于多模式或 XyCAM生成的眼血流数据的纵向比较。