Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer’s diseases using peripheral blood samples and machine learning algorithms

• Published in: Journal of biomedical optics Vol. 25; no. 4; p. 046501
• Main Authors: Salman, Ahmad, Lapidot, Itshak, Shufan, Elad, Agbaria, Adam H, Porat Katz, Bat-Sheva, Mordechai, Shaul
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.04.2020; S P I E - International Society for
• Subjects: Algorithms; Alzheimer's disease; Biomarkers; Dementia; Dementia disorders; Feature selection; Infrared spectroscopy; Learning algorithms; Leukocytes; Lewy bodies; Machine learning; Magnetic resonance imaging; Medical imaging; Medical personnel; Microscopy; Neurodegenerative diseases; Older people; Patients; Peripheral blood; Plasma; Signs and symptoms; Spectrum analysis; Tomography; Germany; dementia with Lewy bodies; WBC; Alzheimer’s disease; plasma; machine learning; infrared spectroscopy
• ISSN: 1083-3668; 1560-2281; 1560-2281
• DOI: 10.1117/1.JBO.25.4.046501

Significance: Accurate and objective identification of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is of major clinical importance due to the current lack of low-cost and noninvasive diagnostic tools to differentiate between the two. Developing an approach for such identification can have a great impact in the field of dementia diseases as it would offer physicians a routine objective test to support their diagnoses. The problem is especially acute because these two dementias have some common symptoms and characteristics, which can lead to misdiagnosis of DLB as AD and vice versa, mainly at their early stages. Aim: The aim is to evaluate the potential of mid-infrared (IR) spectroscopy in tandem with machine learning algorithms as a sensitive method to detect minor changes in the biochemical structures that accompany the development of AD and DLB based on a simple peripheral blood test, thus improving the diagnostic accuracy of differentiation between DLB and AD. Approach: IR microspectroscopy was used to examine white blood cells and plasma isolated from 56 individuals: 26 controls, 20 AD patients, and 10 DLB patients. The measured spectra were analyzed via machine learning. Results: Our encouraging results show that it is possible to differentiate between dementia (AD and DLB) and controls with an ∼86  %   success rate and between DLB and AD patients with a success rate of better than 93%. Conclusions: The success of this method makes it possible to suggest a new, simple, and powerful tool for the mental health professional, with the potential to improve the reliability and objectivity of diagnoses of both AD and DLB.