745 Study of clinical and pathological features in 729 cases of nevus sebaceous

Abstract

Gyeonggi-do, Korea (the Republic of) Introduction : Recently, many researches have been actively conducted to evaluate age by artificial intelligence using various parameters. Prediction of skin age is important because biological age and skin age differ due to various environmental factors, beauty habits, and genetic factors. This study predicted skin age by using multiple regression analysis by five skin parameters that affect aging, and analyzed the predicted age of the group using the First Care Activation Serum and the group without it. Method : 290 women between the ages of 22 and 80 have been recruited in the study. Ninety-five people used First Care Activation Serum for at least three years (Test group), while 195 others did not use (Control group). Subjects were measured by ten parameters (The skin hydration, skin color, gloss, wrinkles, Trans-epidermal water loss, Transparency, Sebum, pH, Melanin and Erythema). Result : Five parameters (The skin hydration, wrinkles, skin color, transparency, and gloss) were changed according to age among ten parameters. Thus, these five parameters were used to perform multiple regression analysis to derive a regression formula for predicting skin age. The regression formula for predicting age using the five skin parameters was suitable for predicting age by having adjusted R-square of 0.685 and showing significant results. Test group was used First Care Activation Serum for an average of 12.05 years, and the predicted skin age was 17.91 years younger than those that did not use product. Discussion : In this study, we developed a formula for predicting skin age using five simple skin measurements. Furthermore, this regression formula is suitable for predicting age because it has 68.5% of the expression's explanatory power. The study also confirmed that skin age may vary depending on beauty habits.