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Showing content from https://pubmed.ncbi.nlm.nih.gov/31524171/ below:

Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy

Affiliations

¹ Department of Chemistry, University at Albany, SUNY, Albany, NY, USA.
² The RNA Institute, College of Arts and Science, University at Albany, SUNY, Albany, NY, USA.
³ Department of Psychology and Neuroscience, Union College, Schenectady, NY, USA.

PMID: 31524171
DOI: 10.3233/JAD-190675

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Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy

Nicole M Ralbovsky et al. J Alzheimers Dis. 2019.

Affiliations

¹ Department of Chemistry, University at Albany, SUNY, Albany, NY, USA.
² The RNA Institute, College of Arts and Science, University at Albany, SUNY, Albany, NY, USA.
³ Department of Psychology and Neuroscience, Union College, Schenectady, NY, USA.

PMID: 31524171
DOI: 10.3233/JAD-190675

Item in Clipboard

Abstract

Background: Alzheimer's disease and related dementias (ADRDs) are being diagnosed at epidemic rates, with incidence to triple from 35 to 115 million cases worldwide. Most ADRDs are characterized by progressive neurodegeneration, and Alzheimer's disease (AD) is the sixth leading cause of death in the United States. The ideal moment for diagnosing ADRDs is during the earliest stages of its progression; however, current diagnostic methods are inefficient, expensive, and unsuccessful at making diagnoses during the earliest stages of the disease.

Objective: The aim of this project was to utilize Raman hyperspectroscopy in combination with machine learning to develop a novel method for the diagnosis of AD based on the analysis of saliva.

Methods: Raman hyperspectroscopy was used to analyze saliva samples collected from normative, AD, and mild cognitive impairment (MCI) individuals. Genetic Algorithm and Artificial Neural Networks machine learning techniques were applied to the spectral dataset to build a diagnostic algorithm.

Results: Internal cross-validation showed 99% accuracy for differentiating the three classes; blind external validation was conducted using an independent dataset to further verify the results, achieving 100% accuracy.

Conclusion: Raman hyperspectroscopic analysis of saliva has a remarkable potential for use as a non-invasive, efficient, and accurate method for diagnosing AD.

Keywords: Alzheimer’s disease; Raman spectroscopy; artificial neural networks; early diagnosis; genetic algorithm; machine learning; mild cognitive impairment; saliva.

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