Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning

被引：37

作者：

Gaudiuso, Rosalba ^{[1
,2
]}

Ewusi-Annan, Ebo ^{[1
]}

Xia, Weiming ^{[2
,3
]}

Melikechi, Noureddine ^{[1
,2
]}

机构：

[1] Univ Massachusetts, Kennedy Coll Sci, Dept Phys & Appl Phys, Lowell, MA 01854 USA

[2] Edith Nourse Rogers Mem Vet Hosp, Geriatr Res Educ Clin Ctr, Bedford, MA 01730 USA

[3] Boston Univ, Dept Pharmacol & Expt Therapeut, Sch Med, Boston, MA 02118 USA

来源：

SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY | 2020年 / 171卷

关键词：

LIBS; DISCRIMINATION; CLASSIFICATION; METALS;

D O I：

10.1016/j.sab.2020.105931

中图分类号：

O433 [光谱学];

学科分类号：

0703 ; 070302 ;

摘要：

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%.

引用

页数：7

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