Machine Learning Methods for Detecting Disease-related Functional and Structural Change in Glaucoma

项目总结 该项目旨在将新的机器学习技术应用于最近发展起来的光学成像 提高青光眼进展的准确预测和检测的措施。复合体 眼科护理提供者日常使用的功能和结构测试包含不完全的隐藏信息 在当前分析中使用，高级模式识别/基于机器学习的分析技术可以 找到并使用隐藏的信息。我们将使用严格的数学技术来发现 并跟踪缺陷在纵向系列周长和光学成像数据中的变化，从多达1,800 病人和健康的眼睛，作为国家卫生研究院长期资助的结果。我们还将深入调查 为此目的学习和新的统计技术。所需的纵向测量来自 几种新开发的光学成像技术是我们以前资助的NEI无法获得的- 支持的工作。 这项拟议的工作可能会显著提高青光眼的内科和外科治疗水平 通过为临床决策提供基于数学基础的信息，降低青光眼护理的成本， 外部验证的方法。此外，改进的青光眼预测和检测技术 进程可用于精细化的受试者招募和确定眼内临床试验的终点 降压和神经保护药物。

PROJECT SUMMARY This project aims to apply novel machine learning techniques to recently developed optical imaging measurement to improve the accurate prediction and detection of glaucomatous progression. Complex functional and structural tests in daily use by eye care providers contain hidden information that is not fully used in current analyses, and advanced pattern recognition/machine learning-based analysis techniques can find and use that hidden information. We will use mathematically rigorous techniques to discover patterns of defects and to track their changes in longitudinal series of perimetric and optical imaging data from up to 1,800 patient and healthy eyes, available as the result of long-term NIH funding. We also will investigate deep learning and novel statistical techniques for this purpose. The required longitudinal measurements from several newly developed optical imaging techniques were not available to our previously funded NEI- supported work. The proposed work potentially can enhance significantly the medical and surgical treatment of glaucoma and reduce the cost of glaucoma care by informing clinical decision-making based on mathematically based, externally validated methods. Moreover, improved techniques for predicting and detecting glaucomatous progression can be used for refined subject recruitment and to define endpoints for clinical trials of intraocular pressure-lowering and neuroprotective drugs.