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

Abstract

IntroductionSoftware 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.ObjectivesTo 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 methodsStructures 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.ResultsFor 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.ConclusionThis 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.

Bayesian‐Edge system for classification and segmentation of skin lesions in Internet of Medical Things

“…This can happen if the sample is rarely presented to the model in such a pose or condition. It can be summarized in Equation (10) , (11) , 36 : where N and R represent the count and ratio for each combination, we can also measure the overall accuracy of the uncertainty estimation as the ratio of the desired cases (i.e., correct–certain and incorrect–uncertain) over all possible cases.…”