TY - JOUR
T1 - Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning
AU - Gaudiuso, Rosalba
AU - Ewusi-Annan, Ebo
AU - Xia, Weiming
AU - Melikechi, Noureddine
N1 - Publisher Copyright:
© 2020
PY - 2020/9
Y1 - 2020/9
N2 - 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%.
AB - 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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U2 - 10.1016/j.sab.2020.105931
DO - 10.1016/j.sab.2020.105931
M3 - Article
AN - SCOPUS:85089032258
SN - 0584-8547
VL - 171
JO - Spectrochimica Acta - Part B Atomic Spectroscopy
JF - Spectrochimica Acta - Part B Atomic Spectroscopy
M1 - 105931
ER -