Predicting and Detecting Glaucomatous Progression Using Pattern Recognition

项目描述（由申请人提供）：本项目旨在通过应用新颖的模式识别技术，提高青光眼进展的准确预测和检测，从而改善青光眼的管理。前提是眼科护理人员日常使用的复杂功能和结构测试包含当前分析中未充分利用的隐藏信息，而先进的模式识别技术可以发现并使用这些隐藏信息。主要目标包括使用数学上严格的技术来发现缺陷的模式，并跟踪它们的变化，这些变化来自多达1800只青光眼和健康眼睛的纵向系列周长和光学成像数据，这些数据是NIH长期资助的结果。有了我们召集的青光眼和模式识别专家的跨学科团队，有了我们广泛的NIH支持的眼睛数据库，有了我们从以前NIH支持的模式识别方法的最佳使用中获得的知识，我们相信我们提出的工作可以显著提高青光眼的内科和外科治疗，降低青光眼的护理成本。此外，预测和检测青光眼进展的改进技术可用于细化受试者招募，并为降低眼压和神经保护药物的临床试验确定终点。

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. PUBLIC HEALTH RELEVANCE: The proposed project will develop and demonstrate the usefulness of pattern recognition techniques for predicting and detecting patterns of glaucomatous change in patient eyes tested longitudinally by visual field and optical imaging instruments. This proposal addresses the current NEI Glaucoma and Optic Neuropathies Program objectives of developing improved diagnostic measures to characterize and detect optic nerve disease onset and characterize glaucomatous neurodegeneration within the visual pathways at structural and functional levels. The development/use of novel, empirical techniques for predicting and detecting glaucomatous progression can have a significant impact on the future of clinical care and the future of clinical trials designed to investigate IOP lowering and neuroprotective drugs.