Novel Glaucoma Diagnostics for Structure and Function - Renewal - 1

针对结构和功能的新型青光眼诊断 - 更新 - 1

基本信息

批准号：
10866656
负责人：
Joel S Schuman
金额：
$ 68.75万
依托单位：
WILLS EYE HEALTH SYSTEM
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-14 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10866656
关键词：
3-Dimensional Blindness Categories Characteristics Clinical Clinical Management Clinical Research Complex Data Detection Development Diagnostic Discrimination Disease Disease Progression Early Diagnosis Early identification Evaluation Eye Floor Future Glaucoma Health Human Image Imaging technology Inner Plexiform Layer Knowledge Laboratories Light Maps Measurable Measurement Measures Metabolic Methodology Modeling Monitor Morbidity - disease rate Optic Disk Optical Coherence Tomography Outcome Oxygen Consumption Oxygen saturation measurement Pathology Research Proposals Resolution Retina Retinal Diseases Scanning Severities Severity of illness Signal Transduction Source Structure Structure-Activity Relationship System Techniques Technology Thick Time Tissue Extracts Tissues Translating Visible Radiation Vision Visual Fields Width advanced disease analytical method clinical practice cohort computerized deep learning density ganglion cell improved in vivo information gathering innovation innovative technologies insight instrument invention knowledge base longitudinal dataset machine learning method macula mathematical methods new technology novel novel strategies ocular imaging preservation prevent programs research study retinal imaging retinal nerve fiber layer tissue oxygenation tool

项目摘要

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

项目摘要青光眼是全球视力发病率和失明的主要原因。早期疾病检测和对进展的敏感监测对于允许及时治疗视力至关重要。这引入眼成像技术可显着提高这些能力，但在临床实践中在疾病严重程度谱的某些阶段仍然存在重大挑战，特别是在早期阶段和晚期疾病。这些困难是由于多种原因在整个过程中发生了变化该疾病，包括大型受试者之间的可变性，继承测量可变性，图像质量，不同的动态测量范围，可测量的组织水平等。在此提案中，我们构建关于我们对眼部成像和建议的长期贡献，可发现青光眼的新颖和敏感手段及其进展已被优化为疾病严重程度的各个阶段。我们将使用信息从视野（功能信息）和领先的眼部成像技术收集 - 光学相干断层扫描（OCT；结构信息）绘制检测整个变化的能力整个疾病的严重程度，以确定疾病每个阶段的最佳参数。两者通常技术和新开发的参数提供的使用参数具有良好的诊断潜力将进行分析。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，以识别与OCT图像中嵌入的结构特征青光眼及其进展没有任何先验假设。这将为结构提供新颖的见解信息，并显示出非常令人鼓舞的初步结果。我们还将利用新成像技术，可见光的OCT，以生成具有出色分辨率的常规图像以提取信息关于组织的氧气满足。这将提供对组织的体内，实时和无创的洞察力功能。综上所述，该程序将推动使用结构和功能信息的使用对青光眼受试者的临床管理的实质性影响