Computer Aided Early Detection and Diagnosis of Alzheimer's Disease

计算机辅助阿尔茨海默病的早期检测和诊断

• 批准号: 7707231
• 负责人: Yong Fan
• 金额: $ 10.32万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-01-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7707231
• 关键词: Adoption; Algorithms; Alzheimer' s Disease; Biometry; Brain; Brain Diseases; Classification; Communities; Computer Assisted; Computer software; Development; Diagnosis; Early Diagnosis; Evolution; Fostering; Functional Imaging; Goals; Image Analysis; Interdisciplinary Study; Measurement; Measures; Medical Imaging; Methods; Neurosciences; Pattern; Research; Research Project Grants; Research Training; Resources; Scientist; Structure; Testing; Training; base; clinical Diagnosis; diagnostic accuracy; disease diagnosis; improved; interoperability; multimodality; neuroimaging; novel; tool

DESCRIPTION (provided by applicant): The goal of this project is to develop the candidate's ability to perform multidisciplinary research in neuroimage analysis, with emphasis on multivariate neuroimage classification and its application to computer aided early detection and diagnosis of Alzheimer's disease (AD). The research project will focus on the development of novel neuroimage classification algorithms for accurately identifying and measuring brain abnormality in a fully automatic and integrated way. The candidate will develop a general integrated neuroimage classification framework by integrating feature extraction, feature selection, and classification. Within this general framework, a multimodality pattern classification method with improved feature extraction from both structural and functional images will be developed. These multimodality multivariate classification methods will be validated and applied to the neuroimage based studies of early AD diagnosis and longitudinal measurement of brain abnormality related to AD. As part of the proposed KOI application, the candidate seeks didactic training in medical imaging, neuroscience, clinical diagnosis of AD, and biostatistics. The proposed training and research plan will foster the candidate's development into an independent scientist, using neuroimaging and image analysis methods for early detection and diagnosis of Alzheimer's disease. RELEVANCE: The improved neuroimage classification methods will help early detection and diagnosis of Alzheimer's disease. The release of the fully automatic neuroimage classification software will significantly improve the interoperability and adoptability of high dimensional pattern classification algorithms for neuroimage analysis, and result in enhanced dissemination, adoption, and evolution of such tools and resources by the broader neuroimaging research community.

描述（由申请人提供）：该项目的目的是发展候选人在神经图像分析中进行多学科研究的能力，重点是多元神经图像分类及其在计算机辅助辅助早期发现和对阿尔茨海默氏病的诊断（AD）的应用。该研究项目将着重于开发新型的神经图像分类算法，以便以全自动和集成的方式准确识别和测量脑异常。候选人将通过集成特征提取，特征选择和分类来开发一般的集成神经图像分类框架。在此一般框架中，将开发一种具有改进的结构和功能图像特征提取的多模式模式分类方法。这些多模式多元分类方法将得到验证，并应用于基于神经图像的早期AD诊断和与AD相关的脑异常的纵向测量的研究。作为拟议的KOI应用的一部分，候选人在医学成像，神经科学，AD的临床诊断和生物统计学方面寻求教学培训。拟议的培训和研究计划将使用神经影像学和图像分析方法来促进候选人的发展，以早日检测和诊断阿尔茨海默氏病。 相关性：改善的神经图像分类方法将有助于早期发现和诊断阿尔茨海默氏病。全自动神经图像分类软件的发布将显着提高高维模式分类算法的互操作性和可采用性，以进行神经图像分析，并通过更广泛的神经成像研究社区来增强这种工具和资源的传播，采用和进化。