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

项目摘要 该项目旨在将新颖的机器学习技术应用于最近开发的光学成像 测量，以提高准确的预测和检测脑昏迷进展。复杂 眼科护理提供者日常使用的功能和结构测试包含隐藏的信息， 先进的模式识别/基于机器学习的分析技术可以 找到并使用这些隐藏的信息。我们将使用严格的数学技术来发现 缺陷，并跟踪其变化的纵向系列周长和光学成像数据，从多达1800 病人和健康的眼睛，作为长期NIH资助的结果。我们也将深入调查 学习和新的统计技术为此目的。所需的纵向测量， 一些新开发的光学成像技术是我们以前资助的NEI无法使用的， 支持工作。 拟议的工作可能会大大提高青光眼的医疗和手术治疗， 通过基于数学的临床决策来降低青光眼护理的成本， 外部验证方法。此外，用于预测和检测神经性昏迷的改进技术， 进展可用于精确的受试者招募，并定义眼内给药临床试验的终点。 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.