Comparing the self-perceived effects of a facial anti-aging product to those automatically detected from selfie images of Chinese women of different ages and cities

Evaluation of adapted dermocosmetic regimens for perimenopausal and menopausal women using an artificial intelligence-based algorithm and quality of life questionnaires: An open observational study

BACKGROUND

The decline in estrogen levels from several years before (perimenopause) and during menopause has various negative effects, including skin specific issues, which often receive less attention than other menopausal symptoms despite having a significant negative effect on quality of life (QoL). The objective of this study was to evaluate the effectiveness of anti-aging dermocosmetic products designed for women during the perimenopause and menopause.

MATERIALS AND METHODS

An open study of 101 perimenopausal women (no menstruation for 4-12 months or irregular menstruation for <5 years) and 101 menopausal women (no menstruation for >12 months), not taking hormone replacement therapy, was conducted. Adapted dermocosmetic regimens, specific to each group (day cream, night cream and serum), were applied for 56 days. Assessments included automatic artificial intelligence diagnostics of eight clinical facial signs, hydration and transepidermal water loss (TEWL), and a menopause skin QoL questionnaire.

RESULTS

Mean age was 50 ± 3.9 years (range 41-57) and 59 ± 3.8 years (range 50-66) for the perimenopause and menopause groups, respectively. Significant improvements in wrinkles and vascular signs, increases in hydration, decreases in TEWL, and a positive impact on QoL were observed after 56 days of application of the respective dermocosmetic regimens for both the perimenopause and menopause groups.

CONCLUSION

The anti-aging skin care products designed specifically for perimenopausal and menopausal women increased skin hydration and improved wrinkles with a positive impact on QoL.

Objective and automatic grading system of facial signs from smartphones' pictures in South African men: Validation versus dermatologists and characterization of changes with age

OBJECTIVE

To evaluate the capacity of the automatic detection system to accurately grade, from selfie pictures, the severity of eight facial signs in South African men.

METHODS

Selfie pictures (obtained from frontal and back cameras) of 281 South African men differently aged (20-70 years) were obtained and analyzed by an automatic artificial intelligence (AI)-based automatic grading system. Data were compared with the clinical gradings made by experts and dermatologists.

RESULTS

In all facial signs, both series of gradings were found highly correlated with, however, different coefficients (0.59-0.95), those of marionette lines and cheek pores being of lower values. No differences were observed between data obtained by frontal and back cameras. With age, in most cases, gradings show up to the 50-59 year age-class, linear-like changes. When compared to men of other ancestries, South African men present lower wrinkles/texture, pigmentation, and ptosis/sagging scores till 50-59 years, albeit not much different in the cheek pores sign. The early onset (mean age) of visibility of wrinkles/texture for South African men were (i.e., reaching grade >1) 39 and 45 years for ptosis/sagging.

CONCLUSION

This study completes and enlarges the previous works conducted on men of other ancestries by showing some South African specificities and slight differences with men of comparable phototypes (Afro American).

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

BACKGROUND

Real-life validation is necessary to ensure our artificial intelligence (AI) skin diagnostic tool is inclusive across a diverse and representative US population of various ages, ancestries and skin phototypes.

OBJECTIVES

To explore the relevance and accuracy of an automated, algorithm-based analysis of facial signs in representative women of different ancestries, ages and phototypes, living in the same country.

METHODS

In a cross-sectional study of selfie images of 1041 US women, algorithm-based analyses of seven facial signs were automatically graded by an AI-based algorithm and by 50 US dermatologists of various profiles (age, gender, ancestry, geographical location). For automated analysis and dermatologist assessment, the same referential skin atlas was used to standardize the grading scales. The average values and their variability were compared with respect to age, ancestry and phototype.

RESULTS

For five signs, the grading obtained by the automated system were strongly correlated with dermatologists' assessments (r ≥ 0.75); cheek skin pores were moderately correlated (r = 0.63) and pigmentation signs, especially for the darkest skin tones, were weakly correlated (r = 0.40) to the dermatologist assessments. Age and ancestry had no effect on the correlations. In many cases, the automated system performed better than the dermatologist-assessed clinical grading due to 0.3-0.5 grading unit differences among the dermatologist panel that were not related to any individual characteristic (e.g. gender, age, ancestry, location). The use of phototypes, as discontinuous categorical variables, is likely a limiting factor in the assessments of grading, whether obtained by automated analysis or clinical assessment of the images.

CONCLUSIONS

The AI-based automatic procedure is accurate and clinically relevant for analysing facial signs in a diverse and inclusive population of US women, as confirmed by a diverse panel of dermatologists, although skin tone requires further improvement.

Virtual approach of the aesthetical fit between hair colours and skin tones in women of different ethnical origin backgrounds

OBJECTIVE

To determine the aesthetical accordance between a given skin tone and the 11 possible colours of head hairs, covered by a marketed hair colouration product.

MATERIAL AND METHODS

The photographs of professional top models, representing several ancestries (non-Hispanic European and Euro-American, East Asian, Hispanic Euro-American, and African-American ancestries), were used to virtually modify skin tones (from light, medium to dark) and hair colour by an artificial intelligence (AI)-based algorithm. Hence, 117 modified photographs were then assessed by five local panels of about 60 women each (one in China, one in France and three in US). The same questionnaire was given to the panels, written in their own language, asking which and how both skin tones and hair colours fit preferentially (or not appreciated), asking in addition the reasons of their choices, using fixed wordings.

RESULTS

Answers from the five panels differed according to origin or cultural aspects, although some agreements were found among both non-Hispanic European and Euro-American groups. The Hispanic American panel in US globally much appreciated darker hair tones (HTs). Two panels (East Asian in China and African American in US) and part of non-Hispanic European panel in France declared appreciating all HTs, almost irrespective with the skin tone (light, medium and dark). This surprising result is very likely caused by gradings (in %) that differ by too low values, making the establishment of a decisive or significant assessment. By nature highly subjective (culturally and/or fashion driven), the assessments should be more viewed as trends, an unavoidable limit of the present virtual approach. The latter offers nevertheless a full respect of ethical rules as such objective could hardly be conducted in vivo: applying 10 or 11 hair colourations on the same individual is an unthinkable option.

CONCLUSION

The virtual approach developed in the present study that mixes two major facial coloured phenotypes seems at the crossroad of both genetic backgrounds and the secular desire of a modified appearance. Nonetheless, this methodology could afford, at the individual level in total confidentiality, a great help to subjects exposed to some facial skin disorders or afflictions.