Predicting the evolution of clinical skin aging in a multi‐ethnic population: Developing causal Bayesian networks using dermatological expertise

• Published in: Skin research and technology Vol. 30; no. 2; pp. e13602 - n/a
• Main Authors: Jouni, Hussein, Jouffe, Lionel, Tancrede‐Bohin, Emmanuelle, André, Pierre, Benamor, Soraya, Cabotin, Pierre‐Patrice, Chen, Jin, Chen, Zekai, Conceiçao, Katleen, Dlova, Ncoza, Figoni‐Laugel, Catherine, Han, Xianwei, Li, Dongni, Pansé, Isabelle, Pavlovic‐Ganascia, Mira, Harvey, Valerie, Ly, Fatimata, Niverd‐Rondelé, Sylvie, Khoza, Nokubonga, Petit, Antoine, Roux, Marie‐Estelle, Shi, Yu, Tardy‐Bastide, Isabelle, Vashi, Neelam, Wang, Shanqing, Wang, Youli, Wu, Jun, Xu, Nan, Yan, Yuehua, Gomes, Charles, Raynaud, Edouard
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.02.2024; John Wiley and Sons Inc
• Subjects: Aging; Algorithms; Bayesian analysis; Belief networks; causal Bayesian networks; Conditional probability; Dermatology; elicitation; Evolution; Forehead; Generative artificial intelligence; Heterogeneity; Mathematical models; Minority & ethnic groups; multi‐ethnic; Original; phototypes; Skin; skin aging; skin aging model; Skin diseases; skin of color; elicitation; causal Bayesian networks; skin aging; multi-ethnic; skin of color; phototypes; skin aging model
• ISSN: 0909-752X; 1600-0846; 1600-0846
• DOI: 10.1111/srt.13602

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.