ABSTRACT
This letter aims to reply to Bratchenko and Bratchenko's comment on our paper "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes." Our paper analyzed the feasibility of using in vivo Raman measurements combined with machine learning techniques to screen diabetic and prediabetic patients. We argued that this approach yields high overall accuracy (94.3%) while retaining a good capacity to distinguish between diabetic (area under the receiver-operating curve [AUC] = 0.86) and control classes (AUC = 0.97) and a moderate performance for the prediabetic class (AUC = 0.76). Bratchenko and Bratchenko's comment focuses on the possible overestimation of the proposed classification models and the absence of information on the age of participants. In this reply, we address their main concerns regarding our previous manuscript.
Subject(s)
Diabetes Mellitus , Prediabetic State , Humans , Prediabetic State/diagnosis , Spectrum Analysis, Raman/methods , Feasibility Studies , Diabetes Mellitus/diagnosis , Machine LearningABSTRACT
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.
Subject(s)
Diabetes Mellitus , Prediabetic State , Diabetes Mellitus/diagnosis , Feasibility Studies , Humans , Prediabetic State/diagnosis , Principal Component Analysis , Spectrum Analysis, Raman/methods , Support Vector MachineABSTRACT
BACKGROUND: Ablative fractional laser surgery is a common technique for treating acne scars. However, an in vivo and noninvasive analysis of the histologic variations between acne skin and the resulting resurfaced skin is needed in order to evaluate the wound healing process of the scars induced by the ablative fractional laser surgery. MATERIALS AND METHODS: Nine patients with acne scars underwent a single treatment with a CO2 ablative fractional laser surgery. Collagen presence on the resurfaced skin was noninvasively assessed by means of Raman spectroscopy and principal component analysis. RESULTS: Principal component analysis shows that all the patients presented a collagen regeneration on the resurfaced skin after the laser treatment. CONCLUSION: Collagen plays a crucial role in the wound healing process. By assessing the collagen presence on the skin, it was possible to quantify the regenerative effects of the ablative fractional laser in a noninvasive way.