OBJECTIVE: To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of ten facial signs in Japanese women and their changes due to age and sun exposures. METHODS: A three-step approach was conducted, based on self-taken selfie images. At first, to check on 310 Japanese women (18-69 years) enrolled in the northerner Hokkaido area (latitude 43.2°N), how, on ten facial signs, the A.I-based automatic grading system may correlate with dermatological assessments, taken as reference. Second, to assess and compare age changes in 310 Japanese and 112 Korean women. Third, as these Japanese panelists were recruited according to their usual behavior toward sun exposure, that is, non-sun-phobic (NSP, N = 114) and sun-phobic (SP, N = 196), and through their regular and early use of a photo-protective product, to characterize the facial photo-damages. RESULTS: (a) On the ten facial signs, detected automatically, nine were found significantly (P < .0001) highly correlated with the evaluations made by three Japanese dermatologists (Wrinkles: r = .75; Sagging: r = .80; Pigmentation: r = .75). (b) The automatic scores showed significant changes with age, by decade, of Wrinkles/Texture, Pigmentation, and Ptosis/Sagging (P < .05). (c) After 45 years, a significantly increased severity of Wrinkles/Texture and Pigmentation was observed in NSP vs. SP women (P < .05). A trend of an increased Ptosis/Sagging (P = .09) was observed. CONCLUSION: This work illustrates, for the first time through investigations conducted at home, some impacts of aging and sun exposures on facial signs of Japanese women. Results significantly confirm the importance of sun avoidance coupled with photo-protective measures. In epidemiological studies, the AI-based system offers a fast, affordable, and confidential approach in detection and quantification of facial signs and their dependence with ages, environments and lifestyles.
OBJECTIVE: To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of ten facial signs in Japanese women and their changes due to age and sun exposures. METHODS: A three-step approach was conducted, based on self-taken selfie images. At first, to check on 310 Japanese women (18-69 years) enrolled in the northerner Hokkaido area (latitude 43.2°N), how, on ten facial signs, the A.I-based automatic grading system may correlate with dermatological assessments, taken as reference. Second, to assess and compare age changes in 310 Japanese and 112 Korean women. Third, as these Japanese panelists were recruited according to their usual behavior toward sun exposure, that is, non-sun-phobic (NSP, N = 114) and sun-phobic (SP, N = 196), and through their regular and early use of a photo-protective product, to characterize the facial photo-damages. RESULTS: (a) On the ten facial signs, detected automatically, nine were found significantly (P < .0001) highly correlated with the evaluations made by three Japanese dermatologists (Wrinkles: r = .75; Sagging: r = .80; Pigmentation: r = .75). (b) The automatic scores showed significant changes with age, by decade, of Wrinkles/Texture, Pigmentation, and Ptosis/Sagging (P < .05). (c) After 45 years, a significantly increased severity of Wrinkles/Texture and Pigmentation was observed in NSP vs. SP women (P < .05). A trend of an increased Ptosis/Sagging (P = .09) was observed. CONCLUSION: This work illustrates, for the first time through investigations conducted at home, some impacts of aging and sun exposures on facial signs of Japanese women. Results significantly confirm the importance of sun avoidance coupled with photo-protective measures. In epidemiological studies, the AI-based system offers a fast, affordable, and confidential approach in detection and quantification of facial signs and their dependence with ages, environments and lifestyles.