From consistent subjective assessment of skin sensitivity severity to its accurate objective scoring.

BACKGROUND: Sensitive skin (SenS) is a syndrome leading to unpleasant sensations with little visible signs. Grading its severity generally relies on questionnaires or subjective ratings. MATERIALS AND METHODS: The SenS status of 183 subjects was determined by trained assessors. Answers from a four-item questionnaire were converted into numerical scores, leading to a 0-15 SenS index that was asked twice or thrice. Parameters from hyperspectral images were used as input for a multi-layer perceptron (MLP) neural network to predict the four-item questionnaire score of subjects. The resulting model was used to evaluate the soothing effect of a cosmetic cream applied to one hemiface, comparing it to that of a placebo applied to the other hemiface. RESULTS: The four-item questionnaire score accurately predicts SenS assessors' classification (92.7%) while providing insight into SenS severity. Most subjects providing repeatable replies are non-SenS, but accepting some variability in answers enables identifying subjects with consistent replies encompassing a majority of SenS subjects. The MLP neural network model predicts the SenS score of subjects with consistent replies from full-face hyperspectral images (R2 Validation set  = 0.969). A similar quality is obtained with hemiface images. Comparing the effect of applying a soothing cosmetic to that of a placebo revealed that subjects with the highest instrumental index (> 5) show significant SenS improvement. CONCLUSION: A four-item questionnaire enables calculating a SenS index grading its severity. Objective evaluation using hyperspectral images with an MLP neural network accurately predicts SenS severity and its favourable evolution upon the application of a soothing cream.

Real-time skin chromophore estimation from hyperspectral images using a neural network.

BACKGROUND: Hyperspectral imaging for in vivo human skin study has shown great potential by providing non-invasive measurement from which information usually invisible to the human eye can be revealed. In particular, maps of skin parameters including oxygen rate, blood volume fraction, and melanin concentration can be estimated from a hyperspectral image by using an optical model and an optimization algorithm. These applications, relying on hyperspectral images acquired with a high-resolution camera especially dedicated to skin measurement, have yielded promising results. However, the data analysis process is relatively expensive in terms of computation cost, with calculation of full-face skin property maps requiring up to 5 hours for 3-megapixels hyperspectral images. Such a computation time prevents punctual previewing and quality assessment of the maps immediately after acquisition. METHODS: To address this issue, we have implemented a neural network that models the optimization-based analysis algorithm. This neural network has been trained on a set of hyperspectral images, acquired from 204 patients and their corresponding skin parameter maps, which were calculated by optimization. RESULTS: The neural network is able to generate skin parameter maps that are visually very faithful to the reference maps much more quickly than the optimization-based algorithm, with computation times as short as 2 seconds for a 3-megapixel image representing a full face and 0.5 seconds for a 1-megapixel image representing a smaller area of skin. The average deviation calculated on selected areas shows the network's promising generalization ability, even on wide-field full-face images. CONCLUSION: Currently, the network is adequate for preview purposes, providing relatively accurate results in a few seconds.