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. 2020 Sep:171:105931.
doi: 10.1016/j.sab.2020.105931. Epub 2020 Jul 15.

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

Rosalba Gaudiuso  1   2 , Ebo Ewusi-Annan  1 , Weiming Xia  2   3 , Noureddine Melikechi  1   2
Affiliations

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

Rosalba Gaudiuso et al. Spectrochim Acta Part B At Spectrosc. 2020 Sep.

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%.

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Conflict of interest statement

Declaration of Competing Interest The authors declare no conflicts of interest. The views expressed in this article are those of the authors and do not represent the views of the US Department of Veterans Affairs or the US Government.

Figures

Fig. 1.
Fig. 1.
a)–b): LIBS spectra of the blood plasma of an AD patient deposited on a Si substrate. The species responsible for the emission of some of the main peaks are identified in the two spectra.
Fig. 2.
Fig. 2.
Model difference spectrum, showing typical net positive and negative peaks that were used for the diagnosis of unknown samples. Some of the difference peaks are assigned in the spectrum.
Fig. 3.
Fig. 3.
a)–b): Difference spectra of two samples, treated as unknowns in the difference spectrum diagnostic test, plotted together with the model spectrum difference. The sample in a) is an AD case, the sample in b) is an HC. The transition at 572.14 nm is Na II emission line while the one at 572.71 nm cannot be identified.
Fig. 4.
Fig. 4.
classification results obtained with QDA with manual feature selection (i.e. by using only the spectral features appearing as positive or negative peaks in the difference spectra.)
See this image and copyright information in PMC

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