利用激光诱导击穿光谱和机器学习诊断阿尔茨海默病。
Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning.
作者信息
Gaudiuso Rosalba, Ewusi-Annan Ebo, Xia Weiming, Melikechi Noureddine
机构信息
Department of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, MA 01854, USA.
Geriatric Research Education Clinical Center, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA 01730, USA.
出版信息
Spectrochim Acta Part B At Spectrosc. 2020 Sep;171. doi: 10.1016/j.sab.2020.105931. Epub 2020 Jul 15.
Abstract
Alzheimer's disease (AD) is a progressive incurable neurodegenerative disease and a major health problem in aging population. We show that the combined use of Laser-Induced Breakdown Spectroscopy (LIBS) and machine learning applied for the analysis of micro-drops of plasma samples of AD and healthy controls (HC) yields robust classification. Following the acquisition of LIBS spectra of 67 plasma samples from a cohort of 31 AD patients and 36 healthy controls (HC), we successfully diagnose late-onset AD (> 65 years old), with a total classification accuracy of 80%, a specificity of 75% and a sensitivity of 85%.
摘要
阿尔茨海默病（AD）是一种进行性、无法治愈的神经退行性疾病，也是老年人群中的一个主要健康问题。我们表明，将激光诱导击穿光谱法（LIBS）与机器学习相结合，用于分析AD患者和健康对照（HC）的血浆样本微滴，可实现可靠的分类。在获取了来自31名AD患者和36名健康对照（HC）队列的67份血浆样本的LIBS光谱后，我们成功诊断出晚发性AD（>65岁），总分类准确率为80%，特异性为75%，灵敏度为85%。
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J Alzheimers Dis. 2018;62(3):1369-1379. doi: 10.3233/JAD-170662.
2
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Biomed Opt Express. 2018 Feb 7;9(3):1057-1068. doi: 10.1364/BOE.9.001057. eCollection 2018 Mar 1.
3
Metals and neurodegenerative diseases. A systematic review.金属与神经退行性疾病。一项系统综述。
Environ Res. 2017 Nov;159:82-94. doi: 10.1016/j.envres.2017.07.048. Epub 2017 Aug 1.
4
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6
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7
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9
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Appl Spectrosc. 2014;68(9):1067-75. doi: 10.1366/14-07488.
10
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