背景:预测衰老对身体外观影响的软件越来越流行。但它没有考虑导致皮肤老化的因素的复杂相互作用。
目的:利用皮肤科医生的专业知识,通过建立因果贝叶斯信念网络(CBBN)来预测皮肤老化临床征象的+15年进展。
方法:结构和条件概率分布来自世界各地至少有15年美学经验的皮肤科医生。CBBN模型建立在所有的光型和年龄范围从18到65岁,专注于皱纹,色素异质性和面部下垂。一组独立的皮肤科医生还对模型进行了评估,以确保外在和内在皮肤老化因素的累积影响的预测质量。特别是初次评估后15年的临床体征评分分布。
结果:为了方便,本文只介绍了非洲皮肤上的模型。详细介绍了前额皱纹的演化模型。这种皮肤类型使用了特定的图谱和面部老化的外在因素。但是预测方法已经对所有的照型进行了验证,以及面部老化的所有临床症状。
结论:该方法提出了一种皮肤老化模型,该模型可预测每种临床体征的老化过程,考虑内生和外生因素。它根据生活方式模拟衰老曲线。它可以用作预防工具,并可以与生成AI算法结合使用,以可视化老化和,潜在的,其他皮肤状况,使用适当的图像。
BACKGROUND: 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.
OBJECTIVE: To predict the +15-year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists.
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.
CONCLUSIONS: 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.