Deep Learning Approaches for Personalized Modeling and Forecasting of Glaucomatous Changes

用于青光眼变化个性化建模和预测的深度学习方法

• 批准号: 10629148
• 负责人: HIROSHI ISHIKAWA
• 金额: $ 38.07万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 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 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629148
• 关键词: 3-Dimensional; Area; Atlases; Blindness; Caring; Clinic; Clinic Visits; Clinical; Clinical Management; Collaborations; Color; Complex; Cross-Sectional Studies; Data; Data Set; Decision Making; Detection; Development; Disease; Disease Progression; Disease model; Early Diagnosis; Eye; Future; Glaucoma; Image; Image Analysis; Imaging technology; Individual; Intervention; Investments; Knowledge; Learning; Location; Machine Learning; Maps; Measurable; Measurement; Medical Imaging; Methods; Modeling; Monitor; Morbidity - disease rate; Ophthalmology; Optical Coherence Tomography; Outcome; Patients; Performance; Research; Research Proposals; Retina; Sampling; Series; Speed; Structure; System; Techniques; Technology; Testing; Thick; Thinness; Time; Tissues; Training; Vision; Visit; Visual Fields; analytical method; brain magnetic resonance imaging; case-by-case basis; clinical application; clinical practice; cohort; computerized; cost; deep learning; deep learning model; falls; feature selection; follow-up; image processing; imaging modality; improved; in vivo; individual patient; innovation; insight; knowledge base; longitudinal analysis; longitudinal dataset; machine learning method; novel; ocular imaging; personalized approach; personalized medicine; personalized predictions; predictive modeling; preservation; prevent; programs; retinal nerve fiber layer; theories; tool; treatment planning; trend

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 managing the optimal care for individual cases due to difficulties of accurately assessing the potential progression and its speed and magnitude. These difficulties are due to a variety of causes that change over the course of the disease, including large inter-subject variability, inherent measurement variability, image quality, varying dynamic ranges of measurements, minimal measurable level of tissues, etc. In this proposal, we propose novel agnostic data-driven deep learning approaches to detect glaucoma and accurately forecast its progression that are optimized to each individual case. 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. Instead of relying on the conventional knowledge-based approaches (e.g. quantifying tissues known to be significantly associated with glaucoma such as retinal nerve fiber layer), the proposed cutting-edge agnostic deep learning approaches determine the features responsible for future structural and functional changes out of thousands of features autonomously by learning from the provided large longitudinal dataset. This program will advance the use of structural and functional information obtained in the clinics with a substantial impact on the clinical management of subjects with glaucoma. Furthermore, the developed methods have potentials to be applied to various clinical applications beyond glaucoma and ophthalmology.

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 由于困难 准确评估潜在的进展及其速度和幅度。这些困难是由于 在整个疾病过程中变化的原因多种多样，包括大型主体间可变性，固有的 测量可变性，图像质量，不同的测量动态范围，可测量的最小水平 在此提案中，我们提出了新的不可知论数据驱动的深度学习方法来检测 青光眼和准确预测其进展已被优化为每种情况。我们将使用状态 - 艺术自动化的计算机化机器学习方法，即深度学习方法，以识别 与青光眼相关的OCT图像中嵌入的结构特征及其进展 没有任何先验假设。这将为结构信息提供新颖的见解，并显示出非常 鼓励初步结果。而不是依靠传统的基于知识的方法（例如 量化已知与青光眼显着相关的组织，例如视网膜神经纤维层）， 拟议的尖端不可知论深度学习方法决定了负责未来的功能 通过从提供的内容中学习的数千个功能的结构和功能变化。 大型纵向数据集。该程序将推动获得获得的结构和功能信息的使用 在诊所中，对青光眼受试者的临床管理产生了重大影响。此外， 开发的方法具有可应用于青光眼以外的各种临床应用和 眼科。