摘要
阿尔茨海默病(AD)的出现,是人口老龄化的日益增加的结果,促使早期诊断方法的有效性日益提高。磁共振成像(MRI)可以作为在体内,非侵入性的工具,以确定敏感和特异性标志物非常早期的阿尔茨海默病进展。近年来,多元模式分析(MVPA)和机器学习算法已经在神经社区的影像引起了浓厚的兴趣,因为,相较于单变量统计分析,它们允许自动分类的高性能成像数据。搜尽文献服务检索系统,科学网和医学索引记录进行这项工作,为了检索的研究主要集中在核磁共振成像在辅助临床医生在阿尔茨海默病早期诊断采用支持向量机(SVM)的潜在作用作为分析的自动分类方法。从2008日至日共有30项研究发表。本综述目的是提供一个关于早期和鉴别诊断的阿尔茨海默病相关的病理学数据最先进的支持向量机的综述,通过核磁共振成像,从初步的步骤,如图像预处理,特征提取和特征选择,以及结束与分类,验证策略和磁共振成像相关的生物标志物提取。对不同技术的主要优点和缺点进行了探讨。所审查的研究结果的分类性能和生物标志物的结果,以揭示的参数,伴随着正常和病理老化的结果。我们最后指出了未解决的问题和未来的发展方向。
关键词: 阿尔茨海默病,自动分类,自动诊断,机器学习,磁共振成像,轻度认知功能障碍,结构神经影像学标志,支持向量机。
Current Alzheimer Research
Title:Frontiers for the Early Diagnosis of AD by Means of MRI Brain Imaging and Support Vector Machines
Volume: 13 Issue: 5
Author(s): Christian Salvatore, Petronilla Battista and Isabella Castiglioni
Affiliation:
关键词: 阿尔茨海默病,自动分类,自动诊断,机器学习,磁共振成像,轻度认知功能障碍,结构神经影像学标志,支持向量机。
摘要: The emergence of Alzheimer’s Disease (AD) as a consequence of increasing aging population makes urgent the availability of methods for the early and accurate diagnosis. Magnetic Resonance Imaging (MRI) could be used as in vivo, non invasive tool to identify sensitive and specific markers of very early AD progression. In recent years, multivariate pattern analysis (MVPA) and machine- learning algorithms have attracted strong interest within the neuroimaging community, as they allow automatic classification of imaging data with higher performance than univariate statistical analysis. An exhaustive search of PubMed, Web of Science and Medline records was performed in this work, in order to retrieve studies focused on the potential role of MRI in aiding the clinician in early diagnosis of AD by using Support Vector Machines (SVMs) as MVPA automated classification method. A total of 30 studies emerged, published from 2008 to date. This review aims to give a state-of-the-art overview about SVM for the early and differential diagnosis of AD-related pathologies by means of MRI data, starting from preliminary steps such as image pre-processing, feature extraction and feature selection, and ending with classification, validation strategies and extraction of MRI-related biomarkers. The main advantages and drawbacks of the different techniques were explored. Results obtained by the reviewed studies were reported in terms of classification performance and biomarker outcomes, in order to shed light on the parameters that accompany normal and pathological aging. Unresolved issues and possible future directions were finally pointed out.
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Christian Salvatore, Petronilla Battista and Isabella Castiglioni , Frontiers for the Early Diagnosis of AD by Means of MRI Brain Imaging and Support Vector Machines, Current Alzheimer Research 2016; 13 (5) . https://dx.doi.org/10.2174/1567205013666151116141705
DOI https://dx.doi.org/10.2174/1567205013666151116141705 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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Current updates on the Role of Neuroinflammation in Neurodegenerative Disorders
Neuroinflammation is an invariable hallmark of chronic and acute neurodegenerative disorders and has long been considered a potential drug target for Alzheimer?s disease (AD) and dementia. Significant evidence of inflammatory processes as a feature of AD is provided by the presence of inflammatory markers in plasma, CSF and postmortem brain ...read more
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