Predicting and Detecting Glaucomatous Progression Using Pattern Recognition

描述（由申请人提供）：该项目旨在通过应用新型模式识别技术来提高青光眼进展的准确预测和检测，从而改善青光眼管理。前提是眼保健提供者日常使用的复杂功能和结构测试包含当前分析中未充分使用的隐藏信息，并且先进的模式识别技术可以找到并使用该隐藏信息。主要目标包括使用严格的数学技术来发现缺陷模式，并跟踪来自多达 1800 只青光眼和健康眼睛的纵向系列视野和光学成像数据的变化，这些数据是 NIH 长期资助的结果。凭借我们组建的青光眼和模式识别专家的跨学科团队、NIH 支持的广泛的眼部数据库以及我们从 NIH 之前的支持中获得的最佳使用模式识别方法的知识，我们相信拟议的工作可以显着增强青光眼的内科和外科治疗，并降低青光眼护理的成本。此外，预测和检测青光眼进展的改进技术可用于精细受试者招募并确定降眼压和神经保护药物临床试验的终点。

DESCRIPTION (provided by applicant): This project aims to improve glaucoma management by applying novel pattern recognition techniques to improve the accurate prediction and detection of glaucomatous progression. The premise is that complex functional and structural tests in daily use by eye care providers contain hidden information that is not fully used in current analyses, and that advanced pattern recognition techniques can find and use that hidden information. The primary goals involve the use of 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 1800 glaucomatous and healthy eyes, available as the result of long-term NIH funding. With the interdisciplinary team of glaucoma and pattern recognition experts we have assembled, with our extensive NIH-supported database of eyes, and with the knowledge we have acquired in the optimal use of pattern recognition methods from previous NIH support, we believe the proposed work can enhance significantly the medical and surgical treatment of glaucoma and reduce the cost of glaucoma care. 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.