Explore our scientific papers and see how we turn AI research into dermatological reality. From 3D facial breakthroughs to precision algorithms, we bridge the gap between AI and skin science, leveraging data to reshape the future of skincare.
Society for Investigative Dermatology
Using ~6,000 participants across three independent cohorts, we developed and externally validated machine learning models to predict multidimensional skin phenotypes from facial images.
Authors: Fudi Wang, Tianzi Liu, Siying Fu, Zhiyang Li, Sijia Wang
Journal of Investigative Dermatology
The nasolabial fold (NLF) is a prominent dermatological phenotype of the aging midface. Previous anatomical studies have clarified that the NLF is potentially induced by the aging changes in the superficial musculoaponeurotic system architecture, cutaneous ligament, midface musculature and fat compartments, and craniofacial skeleton.
Authors: F. Wang, Y. Zhao, X. Hu, R. Ye, L. Du, Z. Li, S. Wang
Society for Investigative Dermatology
Quantifying facial aging is essential in dermatology for studying age-related changes and assessing the effectiveness of skincare products. However, facial aging detection remains challenging due to the absence of standardized benchmarks and unified testing protocols, making it difficult to compare different methods fairly.
Authors: F. Wang, B. Chen, Z. Li
International Federation of Societies of Cosmetic Chemists
Facial aging has long been recognized as an important biomarker of aging and overall health. It is closely linked to numerous age-related diseases, including cardiovascular conditions, metabolic disorders, and neurodegenerative syndromes. As a visible and socially relevant indicator, facial aging has received sustained attention from both scientific communities and the public.
Authors: Fudi Wang, Siying Fu, Baolin Chen, Zhiyang Li, Eagle Lee, Sijia Wang
Journal of Cosmetic Dermatology
To develop a stable and efficient delivery system for baicalin, a flavonoid with potential antioxidant and antiaging properties, to overcome its limitations in solubility, stability, and skin permeability.
Authors: Liang Chen, Fudi Wang, Xiaoyun Hu, Nihong Li, Ying Gao, Fengfeng Xue, Ling Xie, Min Xie
Society for Investigative Dermatology
Facial aging features manifest with considerable inter-individual variability, leading some individuals to appear younger while others appear older. Classic experiments on perceived age rely on human assessment, which demands significant human resources. In this study, we assembled 160 evaluators to assess the perceived age of 3,186 subjects' faces.
Authors: Fudi Wang, and 7 Others
International Society for Investigative Dermatology
Using AI-based skin analysis on a dataset of 431,321 subjects, this study identifies critical age inflection points at which different wrinkle types begin to accelerate. Our large-scale population analysis provides actionable benchmarks for age-specific skincare product development and clinical intervention timing.
Authors: Fudi Wang, Wen Sha, Siying Fu, Xiaoxue Mo, Xinyi Fu, Sijia Wang
International Society for Biophysics and Imaging of the Skin
We present a deep learning algorithm capable of quantifying multiple facial skin aging signs simultaneously from a single photograph. The model achieves dermatologist-level accuracy across key indicators including wrinkles, pigmentation, and skin texture, demonstrating the potential of AI-powered tools for objective skin assessment.
Authors: Sijia Wang
Society for Investigative Dermatology
This preliminary genome-wide association study explores the genetic basis of nasolabial fold variation, identifying candidate loci associated with fold depth and morphology. The findings lay groundwork for understanding how genetic factors influence visible facial aging and support the development of genetically informed skincare solutions.
Authors: Fudi Wang, Yuepu Zhao, Siyuan Du, Jiarui Li, Xiaoyun Hu, Zhiyang Li