Objective and automatic grading system of facial signs from selfie pictures of South African women: Characterization of changes with age and sun‐exposures

Abstract

Objective To evaluate the capacity of the automatic detection system to accurately grade, from smartphones’ selfie pictures, the severity of fifteen facial signs in South African women and their changes related to age and sun‐exposure habits. Methods A two‐steps approach was conducted based on self‐taken selfie images. At first, to assess on 306 South African women (20–69 years) enrolled in Pretoria area (25.74°S, 28.22°E), age changes on fifteen facial signs measured by an artificial intelligence (AI)‐based automatic grading system previously validated by experts/dermatologists. Second, as these South African panelists were recruited according to their usual behavior toward sun‐exposure, that is, nonsun‐phobic (NSP, N = 151) and sun‐phobic (SP, N = 155) and through their regular and early use of a photo‐protective product, to characterize the facial photo‐damages. Results (1) The automatic scores showed significant changes with age, by decade, of sagging and wrinkles/texture (p < 0.05) after 20 and 30 years, respectively. Pigmentation cluster scores presented no significant changes with age whereas cheek skin pores enlarged at a low extent with two plateaus at thirties and fifties. (2) After 60 years, a significantly increased severity of wrinkles/texture and sagging was observed in NSP versus SP women (p < 0.05). A trend of an increased pigmentation of the eye contour (p = 0.06) was observed after 50 years. Conclusion This work illustrates specific impacts of aging and sun‐exposures on facial signs of South African women, when compared to previous experiments conducted in Europe or East Asia. Results significantly confirm the importance of sun‐avoidance coupled with photo‐protective measures to avoid long‐term skin damages. In inclusive epidemiological studies that aim at investigating large human panels in very different contexts, the AI‐based system offers a fast, affordable and confidential approach in the detection and quantification of facial signs and their dependency with ages, environments, and lifestyles.

Accuracy and clinical relevance of an automated, algorithm‐based analysis of facial signs from selfie images of women in the United States of various ages, ancestries and phototypes: A cross‐sectional observational study

“…Meanwhile, to continuously improve both the detection and quantification of pigmentary facial signs, previous datasets were annotated by dermatologists with new markers (e.g. Contrast, Size, Spread maculae, Pigmentation of the eye contour) to train the automatic grading to be more holistic and precise 20,29,31 . As in previous collection campaigns, those studies in Brazil, India, Korea and Japan evaluated consumer perception using self‐reported questionnaires on skin and facial appearance.…”

“…Contrast, Size, Spread maculae, Pigmentation of the eye contour) to train the automatic grading to be more holistic and precise. 20 , 29 , 31 As in previous collection campaigns, those studies in Brazil, India, Korea and Japan evaluated consumer perception using self‐reported questionnaires on skin and facial appearance. Combining this information with dermatologists' annotations could help improve our predictions.…”

“…gender, ancestry, location, lifestyles). 27 , 28 , 29 , 30 , 31 To further validate and enrich this system by identifying areas for improvement and to predict the impacts of new dermocosmetic products on each individual, we designed a study aimed at validating this AI‐based system when applied to subjects from a country with a diverse, multi‐ethnic population, namely the United States of America (USA). Using online recruitment, a completely remote study was designed and conducted to cover four major ancestries over a large age range (18–80 years) and six phototypes, with the objective of exploring the relevance and accuracy of an automated, algorithm‐based analysis of facial signs in women of different ancestries, ages and phototypes.…”

Artificial Intelligence analysis of over half a million European and Chinese women reveals striking differences in the facial skin ageing process

“…15,16 This A.I-based automatic grading system analyses selfie images taken using smartphones and the different datasets used during the development of this algorithm were derived from annotated selfie images from Europe (France, Germany, Spain), North Asia (China, Korea, Japan), India, Brazil, Mexico, Indonesia or the United States. [7][8][9][10][17][18][19] In short, the A.I-based automatic grading system was trained on a scale to predict scores that dermatologists could assess on the same scale.…”

“…We have developed and validated an automatic grading system, based on an A.I algorithm, for grading of the severity of 9 facial signs of ageing (including wrinkles and skin texture, ptosis and sagging, vascular signs, pigmentation signs, cheek skin pores), in men and women of different ethnic origins and ages 15,16 . This A.I‐based automatic grading system analyses selfie images taken using smartphones and the different datasets used during the development of this algorithm were derived from annotated selfie images from Europe (France, Germany, Spain), North Asia (China, Korea, Japan), India, Brazil, Mexico, Indonesia or the United States 7–10,17–19 . In short, the A.I‐based automatic grading system was trained on a scale to predict scores that dermatologists could assess on the same scale.…”