Predicting and Detecting Glaucomatous Progression Using Pattern Recognition

使用模式识别预测和检测青光眼进展

• 批准号: 8216617
• 负责人: CHRISTOPHER BOWD
• 金额: $ 38.68万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8216617
• 关键词: Address; Algorithms; California; Caring; Clinic; Clinical Trials; Clinical Trials Design; Complex; Data; Databases; Defect; Detection; Development; Diagnostic; Disease; Eye; Frequencies; Funding; Future; Glaucoma; Goals; Grant; Image; Imaging Device; Informatics; Knowledge; Laboratories; Lasers; Learning; Machine Learning; Measurement; Measures; Medical; Methods; Modeling; National Eye Institute; Nerve Degeneration; Neuroprotective Agents; Noise; Onset of illness; Operative Surgical Procedures; Ophthalmoscopy; Optic Disk; Optical Coherence Tomography; Patients; Pattern; Pattern Recognition; Perimetry; Physiologic Intraocular Pressure; Physiological; Provider; Scanning; Science; Series; Signal Transduction; Techniques; Technology; Testing; Thick; Time; Translational Research; Treatment Effectiveness; United States National Institutes of Health; Universities; Vision; Vision research; Visual Fields; Visual Pathways; Work; base; clinical care; cost; design; heuristics; improved; independent component analysis; instrument; novel; optic nerve disorder; optical imaging; polarimetry; programs; retinal nerve fiber layer; skills

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.

描述（由申请人提供）：该项目旨在通过应用新的模式识别技术来改善青光眼治疗，以提高青光眼进展的准确预测和检测。前提是眼科护理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这些隐藏信息。主要目标涉及使用数学上严格的技术来发现缺陷的模式，并跟踪它们在纵向系列的周边和光学成像数据的变化，这些数据来自多达1800只昏迷和健康的眼睛，作为NIH长期资助的结果。随着青光眼和模式识别专家的跨学科团队，我们已经组装，与我们广泛的NIH支持的眼睛数据库，并与知识，我们已经获得了最佳使用模式识别方法从以前的NIH支持，我们相信拟议的工作可以大大提高青光眼的医疗和手术治疗，并降低青光眼护理的成本。此外，用于预测和检测青光眼进展的改进技术可用于精确的受试者招募，并定义降低眼内压和神经保护药物临床试验的终点。 公共卫生关系：拟议的项目将开发和展示模式识别技术的有用性，用于预测和检测患者眼睛的青光眼变化模式，通过视野和光学成像仪器进行纵向测试。该提案阐述了当前NEI青光眼和视神经病变项目的目标，即开发改进的诊断措施，以表征和检测视神经疾病发作，并在结构和功能水平上表征视觉通路内的青光眼性神经变性。用于预测和检测青光眼进展的新型经验性技术的开发/使用可能对临床护理的未来以及旨在研究IOP降低和神经保护药物的临床试验的未来产生重大影响。