Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes

In this article, we investigated the feasibility of using Raman spectroscopy and multivariate analysis method to noninvasively screen for prediabetes and diabetes in vivo. Raman measurements were performed on the skin from 56 patients with diabetes, 19 prediabetic patients and 32 healthy volunteers. These spectra were collected along with reference values provided by the standard glycated hemoglobin (HbA1c) assay. A multiclass principal component analysis and support vector machine (PCA-SVM) model was created from the labeled Raman spectra and was validated through a two-layer cross-validation scheme. Classification accuracy of the model was 94.3% with an area under the receiver operating characteristic curve AUC of 0.76 (0.65-0.84) for the prediabetic group, 0.86 (0.71-0.93) for the diabetic group and 0.97(0.93-0.99) for the control group. Our results suggest the feasibility of using Raman spectroscopy for the classification of prediabetes and diabetes in vivo.

Automated summary

This study investigates the feasibility of using Raman spectroscopy and multivariate analysis to noninvasively screen for prediabetes and diabetes. The results suggest that Raman spectroscopy can be used to classify prediabetes and diabetes with high accuracy.