A new procedure, free from human assessment, that automatically grades some facial skin signs in men from selfies pictures. Application to changes induced by a severe aerial chronic urban pollution

OBJECTIVE: These were two folds: at first, to develop an automatic grading system specifically dedicated to some facial signs of men, similar to the one previously validated on women of different ethnic ancestry and second, to assess its potential in detecting and grading the possible impacts of a severe aerial urban pollution on some facial signs of Chinese men. METHODS: In both studies, selfie images were obtained from differently aged men. Nine facial signs were automatically graded through a specific A.I-based algorithm and clinically assessed by a panel of experts and dermatologists. Selfie pictures were taken from individual smartphones of variable optical properties. The first study, designed for developing an automatic grading system, involved three comparable cohorts of men from three different regional ancestries (African, Asian, Caucasian, 110 each) the selfie images of which were acquired under four different lighting conditions. As a second use case study, the facial signs of two cohorts of Chinese men (101 and 100, each), differently aged, regularly exposed to very different aerial urban pollution conditions (UP) were analysed by the same algorithm, selfies being taken under only one lighting condition. RESULTS: -The new automatic grading system of facial signs suits well to men, showing comparable results than that the one dedicated to women and provides data in close agreement with experts' assessments. -In both cases (expert's or automatic methodology), the accuracy of the scores appeared ethnic-dependent. -The applied case confirmed previous results obtained clinically, that is, that many facial signs were found of an increased severity among men exposed to a severe urban pollution, as compared to those living in a less polluted city. -In both studies, statistical agreements between the automatic grading system and expert's assessments were reached. In some facial signs, the automatic grading system seems offering a slightly better accuracy than the assessments made by the experts. CONCLUSION: Apart from some minor limitations, this A.I-based automatic grading system, free from human intervention, performed as well as the one previously developed in women, in close agreement with expert's assessments. In epidemiological studies, this system offers an easy, fast, affordable and confidential approach in the detection and quantification of male facial signs.