Novel Glaucoma Diagnostics for Structure and Function - Renewal - 1

项目摘要 青光眼是世界范围内视力发病和失明的主要原因。早期疾病检测和 敏感地监测进展对于及时治疗以保护视力是至关重要的。的 眼成像技术的引入显著提高了这些能力，但在临床实践中， 在疾病严重程度谱的某些阶段，特别是在早期， 晚期和晚期疾病。这些困难是由于各种各样的原因，这些原因在整个过程中会发生变化。 疾病，包括受试者之间的大的可变性，固有的测量可变性，图像质量， 不同的动态测量范围，最小可测量的组织水平等。在这个建议中，我们建立 我们对眼部成像的长期贡献，并提出新的和敏感的手段来检测青光眼 以及其进展，其针对疾病严重程度的各个阶段进行了优化。我们将使用信息 从视野（功能信息）和领先的视觉成像技术-光学 相干断层扫描（OCT;结构信息），以映射检测跨 整个疾病严重程度谱，以确定疾病每个阶段的最佳参数。两者通常 所使用的技术参数和新开发的具有良好诊断潜力的参数 将被分析。我们将使用最先进的自动计算机化机器学习方法，即 深度学习方法，以识别OCT图像中嵌入的结构特征， 青光眼及其进展，而无需任何先验假设。这将提供新的见解， 我们已经取得了令人鼓舞的初步成果。我们还将利用一种新的成像技术 技术，可见光OCT，以生成具有出色分辨率的视网膜图像来提取信息 组织的氧饱和度这将提供对组织的体内、真实的时间和非侵入性洞察 功能.总而言之，该计划将通过以下方式促进结构和功能信息的使用 对青光眼受试者的临床管理产生实质性影响

Project Summary Glaucoma is a leading cause of vision morbidity and blindness worldwide. Early disease detection and sensitive monitoring of progression are crucial to allow timely treatment for preservation of vision. The introduction of ocular imaging technologies significantly improves these capabilities, but in clinical practice there are still substantial challenges at certain stages of the disease severity spectrum, specifically in the early stage and in advanced disease. These difficulties are due to a variety of causes that change over the course of the disease, including large between-subject variability, inherent measurement variability, image quality, varying dynamic ranges of measurements, minimal measurable level of tissues, etc. In this proposal, we build on our long-standing contribution to ocular imaging and propose novel and sensitive means to detect glaucoma and its progression that are optimized to the various stages of disease severity. We will use information gathered from visual fields (functional information) and a leading ocular imaging technology – optical coherence tomography (OCT; structural information) to map the capability of detecting changes across the entire disease severity spectrum to identify optimal parameters for each stage of the disease. Both commonly used parameters provided by the technologies and newly developed parameters with good diagnostic potential will be analyzed. We will use state-of-the-art automated computerized machine learning methods, namely the deep learning approach, to identify structural features embedded within OCT images that are associated with glaucoma and its progression without any a priori assumptions. This will provide novel insight into structural information, and has shown very encouraging preliminary results. We will also utilize a new imaging technology, the visible light OCT, to generate retinal images with outstanding resolution to extract information about the oxygen saturation of the tissue. This will provide in-vivo, real time, and noninvasive insight into tissue functionality. Taken together, this program will advance the use of structural and functional information with a substantial impact on the clinical management of subjects with glaucoma