Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model

Abstract

Raman spectroscopy is a powerful method for estimating the molecular structure of a target that can be adapted for biomedical analysis given its non-destructive nature. Inflammatory skin diseases impair the skin's barrier function and interfere with the patient's quality of life. There are limited methods for non-invasive and objective assessment of skin inflammation. We examined whether Raman spectroscopy can be used to predict skin inflammation with high sensitivity and specificity when combined with artificial intelligence (AI) analysis. Inflammation was chemically induced in mouse ears, and Raman spectra induced by a 785 nm laser were recorded. A principal component (PC) analysis of the Raman spectra was performed to extract PCs with the highest percentage of variance and to estimate the statistical score. The accuracy in predicting inflammation based on the Raman spectra with or without AI analysis was assessed using receiver operating characteristic (ROC) curves. We observed some typical changes in the Raman spectra upon skin inflammation, which may have resulted from vasodilation and interstitial oedema. The estimated statistical scores based on spectral changes correlated with the histopathological changes in the skin. The ROC curve based on PC2, which appeared to include some spectral features, revealed a maximum accuracy rate of 80.0% with an area under the curve (AUC) of 0.864. The AI analysis improved the accuracy rate to 93.1% with an AUC of 0.972. The current findings demonstrate that the combination of Raman spectroscopy with near-infrared excitation and AI analysis can provide highly accurate information on the pathology of skin inflammation.