Evaluation of facial skin age based on biophysical properties in vivo

OBJECTIVE: The evaluation of skin age, reflecting overall facial characteristics, has not been established. Previous studies focused on visual assessment or individual-specific feature such as wrinkles or skin color. We studied the evaluation model of skin age index (SAI) including the overall aging features including wrinkles, skin color, pigmentation, elasticity, and hydration. METHODS: ) and root mean square error (RMSE). Validation study of the SAI model performed on 24 women for 6 weeks application with anti-aging product. RESULTS: and lowest RMSE compared to other models, such as svmRadial, gaussprRadial, blackboost, rpart, and statistical regression formula. The cforest prediction model confirmed a significant decrease of predicted SAI after 6 weeks of application of anti-aging product. CONCLUSION: We developed a prediction model to evaluate a SAI using machine learning, and led to accurate predicted age for overall clinical aging. This model can a good standard index for evaluating facial skin aging and anti-aging products.