The colour of the human skin: fruitful science, unsuitable wordings

The relevance and accuracy of an AI algorithm-based descriptor on 23 facial attributes in a diverse female US population

BACKGROUND

The response of AI in situations that mimic real life scenarios is poorly explored in populations of high diversity.

OBJECTIVE

To assess the accuracy and validate the relevance of an automated, algorithm-based analysis geared toward facial attributes devoted to the adornment routines of women.

METHODS

In a cross-sectional study, two diversified groups presenting similar distributions such as age, ancestry, skin phototype, and geographical location was created from the selfie images of 1041 female in a US population. 521 images were analyzed as part of a new training dataset aimed to improve the original algorithm and 520 were aimed to validate the performance of the AI. From a total 23 facial attributes (16 continuous and 7 categorical), all images were analyzed by 24 make-up experts and by the automated descriptor tool.

RESULTS

For all facial attributes, the new and the original automated tool both surpassed the grading of the experts on a diverse population of women. For the 16 continuous attributes, the gradings obtained by the new system strongly correlated with the assessment made by make-up experts (r ≥ 0.80; p < 0.0001) and supported by a low error rate. For the seven categorical attributes, the overall accuracy of the AI-facial descriptor was improved via enrichment of the training dataset. However, some weaker performance in spotting specific facial attributes were noted.

CONCLUSION

In conclusion, the AI-automatic facial descriptor tool was deemed accurate for analysis of facial attributes for diverse women although some skin complexion, eye color, and hair features required some further finetuning.

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.

Trihydroxybenzoic acid glucoside as a global skin color modulator and photo-protectant

Background

3,4,5-Trihydroxybenzoic acid glucoside (THBG), a molecule produced by an original biocatalysis-based technology, was assessed in this study with respect to its skin photoprotective capacity and its skin color control property on Asian-type skin at a clinical level and on skin explant culture models.

Methods

The double-blinded clinical study was done in comparison to a vehicle by the determination of objective color parameters thanks to recognized quantitative and qualitative analysis tools, including Chroma-Meter, VISIA-CR™, and SIAscope™. Determination of L* (brightness), a* and b* (green–red and blue–yellow chromaticity coordinates), individual typology angle, and C* (chroma) and h* (hue angle) parameters using a Chroma-Meter demonstrated that THBG is able to modify skin color while quantification of ultraviolet (UV) spots by VISIA-CR™ confirmed its photoprotective effect. The mechanism of action of THBG molecule was determined using explant skin culture model coupled to histological analysis (epidermis melanin content staining).

Results

We have demonstrated that THBG was able to modulate significantly several critical parameters involved in skin color control such as L* (brightness), a* (redness), individual typology angle (pigmentation), and hue angle (yellowness in this study), whereas no modification occurs on b* and C* parameters. We have demonstrated using histological staining that THBG decrease epidermis melanin content under unirradiated and irradiated condition. We also confirmed that THBG molecule is not a sunscreen agent.

Conclusion

This study demonstrated that THBG controls skin tone via the inhibition of melanin synthesis as well as the modulation of skin brightness, yellowness, and redness.