OBJECTIVE: To develop an automatic system that grades the severity of facial signs through 'selfies' pictures taken by women of different ages and ethnics. METHODS: 1140 women from three ethnics (African-American, Asian, Caucasian), of different ages (18-80 years old), took 'selfies' by high resolution smartphones cameras under different conditions of lighting or facial expressions. A dedicated software, was developed, based on a Convolutional Neural Network (CNN) that integrates training data from referential Skin Aging Atlases. The latter allows to an immediate quantification of the severity of nine facial signs according to the ethnicity declared by the subject. These automatic grading were confronted to those assessed by 12 trained experts and dermatologists either on 'selfies' pictures or in live conditions on a smaller cohort of women. RESULTS: The system appears weakly influenced by lighting conditions or facial expressions (coefficients of variations ranging 10-13% for most signs) and leads to global agreements with experts' assessments, even showing a better reproducibility on some facial signs. CONCLUSION: This automatic scoring system, still in development, seems offering a new quantitative approach in the quantified description of facial signs, independent from human vision, in many applications, being individual, cosmetic oriented or dermatological with regard to the follow-up of medical anti-ageing corrective strategies. a des valeurs comparables de celles des experts, voire même de meilleure reproductibilit e dans certains cas. CONCLUSION: Ce syst eme de scorage automatique, encore en d eveloppement, semble offrir une nouvelle approche dans la description quantitative de signes du visage, ind ependante de l'oeil humain, dans de nombreuses applications, comme la personnalisation, a vis ee cosm etique ou dermatologique, dans le suivi de certaines strat egies m edicales de l'antivieillissement cutan e.

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.

Objective To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of seven new facial signs added to the nine previously integrated. Methods A two‐step approach was conducted: first, to check on 112 Korean women, how the AI‐based automatic grading system may correlate with dermatological assessments, taken as reference; second, to confirm on 1140 women of three ancestries (African, Asian, and Caucasian) the relevance of the newly input facial signs. Results The sixteen specific Asian facial signs, detected automatically, were found significantly (P < .0001) highly correlated with the clinical evaluations made by two Korean dermatologists (wrinkles: r = .90; sagging: r = .75‐.95; vascular: r = .85; pores: r = .60; pigmentation: r = .50‐.80). When applied at a larger scale on women of different ethnicities, new signs were found of good accuracy and reproducibility, albeit depending on ethnicity. Due to contrast with the innate skin complexion, the facial signs dealing with skin pigmentation were found of a much higher relevance among Asian women than African or Caucasian women. The automatic gradings were even found of a slightly higher accuracy than the clinical gradings. Conclusion The previously used automatic grading system is now completed by adding new facial signs apt at being detected. The continuous development is now integrating some limitations with regard to the constitutive skin complexion of the self‐pictured subjects. Presenting reproducible assessments, highly correlated with medical grading, this system could change tremendously clinical researches, like in epidemiological studies, where it offers an easy, fast, affordable, and confidential approach in the objective quantification of facial signs.

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