Novel Glaucoma Diagnostics for Structure and Function - Renewal - 1

项目摘要 青光眼是世界范围内导致视力障碍和失明的主要原因。及早发现疾病和 对进展的灵敏监测对于及时治疗以保护视力至关重要。这个 眼科成像技术的引入显著提高了这些能力，但在临床实践中 在疾病严重程度的某些阶段，特别是在早期，仍然存在重大挑战。 晚期和晚期。这些困难是由各种原因造成的，这些原因在 疾病，包括较大的受试者间变异性、固有测量变异性、图像质量、 测量的不同动态范围、组织的最小可测量水平等。在此建议中，我们建立 关于我们长期以来对眼睛成像的贡献，并提出新的和敏感的方法来检测青光眼 以及根据疾病严重程度的不同阶段进行优化的进展。我们将使用信息 从视野(功能信息)和领先的眼部成像技术--光学 相干层析成像(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。两者通常都是 使用技术提供的参数和新开发的具有良好诊断潜力的参数 将会被分析。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，以识别嵌入在OCT图像中的与 没有任何先验假设的青光眼及其进展。这将提供对结构的新见解 信息，并显示了非常令人鼓舞的初步结果。我们还将利用一种新的成像技术 可见光OCT技术，生成分辨率出众的视网膜图像，提取信息 关于组织的氧饱和度。这将提供体内、实时和非侵入性的组织洞察 功能性。综上所述，该程序将通过一个 对青光眼患者临床治疗的重大影响

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