Estimation of skin age and phototype using bimodal spectroscopy and machine learning

Information on skin phototype and ages is of cosmetic and medical interest in some procedures like objective evaluation of cosmetic treatments effectiveness, laser wavelength choice, risk of skin cancer recurrence and skin evaluation before cosmetic surgeries. Phototype may be evaluated using the Fitzpatrick questionnaire whose results are impaired by patients’ subjective answers; melanometers may be used but are not always available in dermatology practice. Tewameter, corneometer or cutometer are used to evaluate skin features that may be related to skin age but they lack evaluation of skin internal structure directly related to skin age (fibrosis, elastosis, etc.). Optical spectroscopy combining autofluorescence (AF) and diffuse reflectance (DR) may be a promising and non-invasive alternative to these tests.

In the current study, a bimodal spectroscopic device was used to obtain in vivo spatially resolved AF and DR spectra of skin in the visible range. Five LEDs featuring wavelength peaks at 365, 385, 395, 400 and 415 nm and a xenon lamp featuring a 350-800 nm spectral emission were used as light sources. Four source-detector separation (SDS) were used: 400, 600, 800, and 1000 μm.

Spectra were taken in different anatomical sites on 131 patients of different age and gender during a clinical study. Spectra were analysed using classification (support vector machine and multilayer perceptron) and regression (multilayer perceptron, linear, kernel ridge and Lasso) methods. Results of skin phototype and age estimation from AF and DR spectra obtained in vivo using machine learning methods will be presented and discussed.