Machine Learning Assisted Handheld Confocal Raman Micro-Spectroscopy for Identification of Clinically Relevant Atopic Eczema Biomarkers

Atopic dermatitis (AD) is a common chronic inflammatory skin dermatosis condition due to skin barrier dysfunction that causes itchy, red, swollen, and cracked skin. Currently, AD severity clinical scores are subjected to intra- and inter-observer differences. There is a need for an objective scoring...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (13), p.4674
Main Authors: Dev, Kapil, Ho, Chris Jun Hui, Bi, Renzhe, Yew, Yik Weng, S, Dinish U., Attia, Amalina Binte Ebrahim, Moothanchery, Mohesh, Guan, Steven Thng Tien, Olivo, Malini
Format: Article
Language:English
Subjects:
Amino acids
atopic eczema
Biomarkers
Classification
confocal Raman spectroscopy
Dermatitis
Discriminant analysis
Eczema
Hydration
Lasers
Lipids
Machine learning
Mutation
partial least squares discriminant analysis
Proteins
Raman spectra
Review boards
Skin
Spectral sensitivity
Spectroscopy
Spectrum analysis
variable importance in projection
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Summary:Atopic dermatitis (AD) is a common chronic inflammatory skin dermatosis condition due to skin barrier dysfunction that causes itchy, red, swollen, and cracked skin. Currently, AD severity clinical scores are subjected to intra- and inter-observer differences. There is a need for an objective scoring method that is sensitive to skin barrier differences. The aim of this study was to evaluate the relevant skin chemical biomarkers in AD patients. We used confocal Raman micro-spectroscopy and advanced machine learning methods as means to classify eczema patients and healthy controls with sufficient sensitivity and specificity. Raman spectra at different skin depths were acquired from subjects’ lower volar forearm location using an in-house developed handheld confocal Raman micro-spectroscopy system. The Raman spectra corresponding to the skin surface from all the subjects were further analyzed through partial least squares discriminant analysis, a binary classification model allowing the classification between eczema and healthy subjects with a sensitivity and specificity of 0.94 and 0.85, respectively, using stratified K-fold (K = 10) cross-validation. The variable importance in the projection score from the partial least squares discriminant analysis classification model further elucidated the role of important stratum corneum proteins and lipids in distinguishing two subject groups.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22134674