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

Abstract

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.

Self‐recording the skin hydration and Trans‐Epidermal Water Loss parameters: A pilot study

“…The tremendous and recent advances made in the connecting processes through smartphones, developing skin measuring connected devices (skin color, sebum production, mechanical properties…) seems a realistic future step. Merging nomad devices, wearable sensors, 15,16 and new A.I‐based algorithms for automatic clinical grading of smartphones images 17‐20 seems a new pathway for monitoring of women and men in their real environments and lifestyles. However, to ensure a collection of reliable data, this approach strictly requires the subjects enrolled in a given study to be fully informed and trained about the proper handling of these connected devices.…”

The continuous development of a complete and objective automatic grading system of facial signs from selfie pictures: Asian validation study and application to women of three ethnic origins, differently aged

“…The development of artificial intelligence and deep learning‐based algorithms is a growing and fascinating approach, being for medical purposes 1 or cosmetical applications. As for the latter, as previously published, 2,3 an automatic grading system, based on an artificial intelligence (AI) algorithm, allowed to attribute the grading of the severity of nine facial signs (Wrinkles and Skin texture, Ptosis and Sagging, Vascular signs, Pigmentation signs, and Cheek skin pores), on men and differently aged men and women of different ethnic ancestries. As this system performs its analysis from smartphones' selfie pictures, it was found not only reliable (ie, rather independent from lighting conditions or angle in the shooting), but its gradings correlated well with expert's assessments and dermatologists 2‐4 .…”

“…As for the latter, as previously published, 2,3 an automatic grading system, based on an artificial intelligence (AI) algorithm, allowed to attribute the grading of the severity of nine facial signs (Wrinkles and Skin texture, Ptosis and Sagging, Vascular signs, Pigmentation signs, and Cheek skin pores), on men and differently aged men and women of different ethnic ancestries. As this system performs its analysis from smartphones' selfie pictures, it was found not only reliable (ie, rather independent from lighting conditions or angle in the shooting), but its gradings correlated well with expert's assessments and dermatologists 2‐4 . Gradings that were found ethnic‐dependent as uncertain (eg, vascular signs among African subjects or cheek skin pores in Asian subjects) were logically found of increased values with age in both genders, showing some different rates of progression, ethnic and gender‐related.…”

“…Gradings that were found ethnic‐dependent as uncertain (eg, vascular signs among African subjects or cheek skin pores in Asian subjects) were logically found of increased values with age in both genders, showing some different rates of progression, ethnic and gender‐related. As applied study, this system allowed to show that many facial signs are of an increased severity among Chinese men exposed to a severe aerial pollution, 3 as compared to Chinese men living in a less polluted environment. In addition, this automatic grading system seems to offer, in some facial signs, a slightly better accuracy than the assessments made by experts or dermatologists.…”

Gender‐related differences in the facial aging of Caucasian French subjects and their relations with perceived ages and tiredness

“…Whether this aspect could be extended to other ethnicities is an issue that calls for future studies. To such objective, a recent methodology, using an automatic recognition and quantification of facial signs from selfies, [23][24][25] would certainly be of a great help since easily applied to much larger cohorts of subjects of different ethnic ancestries.…”