Abstract

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.