{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T10:03:25Z","timestamp":1790330605728,"version":"4.1.0"},"reference-count":29,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2019R1A6C1010033"],"award-info":[{"award-number":["2019R1A6C1010033"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["RS\u20102026\u201025539507"],"award-info":[{"award-number":["RS\u20102026\u201025539507"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate an artificial intelligence (AI)\u2013based skin assessment framework capable of predicting both dermatologist\u2010assigned visual grades and device\u2010derived physiological measurements from facial images. In addition, we evaluated the impact of image\u2010acquisition modality (DSLR, tablet, or smartphone) on model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1,099 Korean participants aged 14\u201369 years were enrolled. High\u2010resolution facial images were obtained from seven standardized angles using a DSLR camera, and a subset of additional images was collected using a tablet and participant\u2010owned smartphones. Five board\u2010certified dermatologists graded eight facial signs, and physiological parameters were measured using non\u2010invasive skin assessment devices. CoAtNet\u20104 served as the backbone architecture for both classification (visual grading) and regression (physiological prediction). Model performance was assessed using mean absolute error and correlation analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>When evaluated using DSLR images, the model achieved a mean exact\u2010grade accuracy of 51.4%, with an average of 93.3% of predictions falling within \u00b1 1 grade across facial signs. High correlations were observed for wrinkles, pigmentation, pores, and sagging, whereas lip dryness demonstrated comparatively lower correlations. For physiological metrics, strong correlations were observed for pigmentation spot count, cheek pore visibility, and wrinkle severity, whereas hydration and elasticity showed moderate correlations. Performance on mobile\u2010device images remained high and showed strong agreement with DSLR\u2010based predictions, although a noticeable decline in pigmentation\u2010related accuracy was observed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed AI framework reliably approximates dermatologist visual grading and multiple device\u2010based physiological measurements, offering a comprehensive, image\u2010driven approach to skin aging assessment. However, model performance varies across facial attributes and remains sensitive to image quality, emphasizing the need for domain adaptation and image enhancement strategies to ensure robust application in consumer environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/srt.70375","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:35:47Z","timestamp":1788240947000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Based Skin Analysis Models for Predicting Visual Grades and Device Measured Physiological Values From Facial Images"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5098-777X","authenticated-orcid":false,"given":"Eunyoung","family":"Lee","sequence":"first","affiliation":[{"name":"Institut d'Expertise Clinique (IEC) Korea Suwon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1132-3362","authenticated-orcid":false,"given":"Jeongho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Dankook University  Yongin Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nahee","family":"Kim","sequence":"additional","affiliation":[{"name":"Institut d'Expertise Clinique (IEC) Korea Suwon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junchae","family":"Na","sequence":"additional","affiliation":[{"name":"Kailoslab Co., Ltd  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byungcheol","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Dermatology Dankook University  Cheonan Chungcheongnam\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0462-0050","authenticated-orcid":false,"given":"Sang\u2010Il","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Dankook University  Yongin Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-3083.2010.03963.x"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2133.1975.tb05113.x"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/archderm.1991.04520010052006"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2133.1994.tb02895.x"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1473-2130.2004.00092.x"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/jocd.14653"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/jocd.13305"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.18880"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.18073"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/ics.12512"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/ics.12924"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13555-021-00541-9"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.08.006"},{"key":"e_1_2_11_16_1","unstructured":"A.Vaswani N.Shazeer N.Parmar et\u00a0al. \u201cAttention Is All You Need \u201dAdvances in Neural Information Processing Systems30(2017):5998\u20136008."},{"key":"e_1_2_11_17_1","first-page":"3965","article-title":"CoAtNet: Marrying Convolution and Attention for all Data Sizes","volume":"34","author":"Dai Z.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_18_1","unstructured":"C.Lugaresi J.Tang andH.Nash MediaPipe: A framework for building perception pipelines(arXiv 2019) arXiv:1906.08172."},{"key":"e_1_2_11_19_1","volume-title":"Skin Aging Atlas 2 Asian Type","author":"Bazin R.","year":"2010"},{"key":"e_1_2_11_20_1","doi-asserted-by":"crossref","unstructured":"K.He X.Zhang S.Ren andJ.Sun \u201cDeep Residual Learning for Image recognition \u201dProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2016):770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_21_1","unstructured":"A.Dosovitskiy L.Beyer A.Kolesnikov et\u00a0al. 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