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Introduction: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging., Objectives: To predict the +15-year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists., Material and Methods: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment., Results: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging., Conclusion: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images., (© 2024 L'Oréal. Skin Research and Technology published by John Wiley & Sons Ltd.)

OBJECTIVE: There are clinical differences in healthy skin requirements and skin-aging features by race and ethnicity. However, individuals of color are underrepresented in dermatology-related medical information. We sought to gather information from women of color regarding their attitudes about the importance of the prevention of skin aging, available information, and perception of representation in skin-aging prevention information. METHODS: This study involved an observational, cross-sectional, online survey of women aged 18 to 70 years residing in the United States. Participants were placed into one of seven cohorts based on self-reported race/ethnicity. Relative frequencies of responses were compared across cohorts; adjusted logistic regression was used to assess perception of representation in skin-aging prevention information. RESULTS: The mean age of the 1,646 participants was 44.4 years. The mean (standard deviation) rating (from 0, "not at all important" to 10, "extremely important") of the importance of the prevention of skin aging ranged from 7.3 to 8.2 across the seven cohorts. All cohorts reported the most trusted source of information for skin-aging prevention products and treatments was a skin-care professional, but not all cohorts believed they are well represented in available sources of information. Older age, lower median household income, and a race/ethnicity of Black, Asian, "Other," and "More Than One Race" were less likely to report being well represented. LIMITATIONS: People without internet access could not participate, potentially excluding some older and lower-income groups. CONCLUSION: Women of color are less likely to feel represented in available information on the prevention of skin aging. [ABSTRACT FROM AUTHOR]

The article titled "Back Cover" is based on the original article "Predicting the evolution of clinical skin aging in a multi-ethnic population: Developing causal Bayesian networks using dermatological expertise" by Hussein Jouni et al. The study focuses on predicting the progression of skin aging in a diverse population using Bayesian networks. The authors include a wide range of experts in the field of dermatology and explore the impact of various factors on skin aging. This research provides valuable insights into understanding and addressing skin aging in different ethnic groups. [Extracted from the article]