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

Autor: Jouni H; L'Oreal Research and Innovation, Clichy, France., Jouffe L; Bayesia S.A.S., Change, France., Tancrede-Bohin E; L'Oreal Research and Innovation, Clichy, France.; Dermatology Department St Louis Hospital, APHP, Paris, France., André P; Paris-Université Laser Skin Clinic, Paris, France., Benamor S; Dermatologist, Private Practice, Paris, France., Cabotin PP; Dermatologist, Private Practice, Clichy, France., Chen J; The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Chen Z; Huizhou First Maternal and Child Health Hospital, Huizhou, China., Conceiçao K; Black Skin Dermatology, Paula Bellotti Group, Rio de Janeiro, Brazil., Dlova N; Dermatology Department, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa., Figoni-Laugel C; Dermatologist, Private Practice, Boulogne-Billancourt, France., Han X; Shenyang Seventh People's Hospital, Shanghai, China., Li D; Guangdong Second People's Hospital, Guangdong, China., Pansé I; Dermatologist, Private Practice, Chatou, France., Pavlovic-Ganascia M; Dermatology Department St Louis Hospital, APHP, Paris, France., Harvey V; Hampton Roads Center for Dermatology, Newport News, Virginia, Skin of Color Research Institute, Hampton University, Hampton, Virginia, USA., Ly F; Dermatology and Venerology, Cheikh Anta Diop University, Dakar, Senegal., Niverd-Rondelé S; Dermatologist, Private Practice, Corbeil-Essone, France., Khoza N; Dermatologist, Private Practice, Durban, South Africa., Petit A; Dermatology and Venereology Department, Saint-Louis Hospital, Paris Cité University, Paris, France., Roux ME; Dermatologist, Private Practice, Paris, France., Shi Y; Shanghai Dermatology Hospital, Shanghai, China., Tardy-Bastide I; Dermatologist, Private Practice, Levallois-Perret, France., Vashi N; Dermatology Department, Boston University Chobanian & Avedisian School of Medicine, Boston, USA., Wang S; Dermatology Department, Ruijin Hospital Shanghai Jiao Tong University School of Medicine, Shanghai, China., Wang Y; Zhuji Traditional Chinese Medicine Hospital, Zhejiang, China., Wu J; L'Oréal Research and Innovation, Shanghai, China., Xu N; Shanghai Oriental Hospital, Shanghai, China., Yan Y; Fudan University Pudong Hospital, Shanghai, China., Gomes C; L'Oreal Research and Innovation, Clichy, France., Raynaud E; L'Oreal Research and Innovation, Clichy, France.; CRB St Louis Hospital, Paris, France.
Jazyk: angličtina
Zdroj: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) [Skin Res Technol] 2024 Feb; Vol. 30 (2), pp. e13602.
DOI: 10.1111/srt.13602
Abstrakt: 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.)
Databáze: MEDLINE
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