Medical Image Computing and Computer Assisted Intervention
Mental health screening relies on subjective questionnaires, leaving objective biological signals uncaptured. Grounded in the "skin-brain axis," we present Skin2Mind, the first psychodermatology-based multimodal AI framework for individual mental health risk prediction.
著者： Jiahe Liu et al.
Scientific Reports
This study aimed to describe facial dermal exposure-related parameters and skincare behaviors among adult women in China and to explore their associations with sensitive skin status and objective skin biophysical measurements.
著者： Rong Hu et al.
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.
著者： Fudi Wang et al.
Journal of Investigative Dermatology
This study conducted the first genome-wide association study specifically targeting the nasolabial fold, identifying multiple novel genetic variants significantly associated with its morphological characteristics. Using a large cohort and advanced imaging-based phenotyping, we reveal the genetic architecture underlying this key facial aging trait and provide new insights into the biology of facial morphology.
著者： Fudi Wang et al.
Society for Investigative Dermatology
We systematically evaluate multiple imaging-based approaches for automated facial aging detection, comparing deep learning architectures across diverse demographic groups. Our findings highlight the strengths and limitations of current methods and propose a standardized evaluation framework for future research in dermatological imaging.
著者： Fudi Wang et al.
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.
著者： Fudi Wang et al.
Journal of Cosmetic Dermatology
We developed a novel bio-vesicle system for the transdermal delivery of baicalin and evaluated its anti-aging efficacy through clinical trials. Results demonstrate significantly improved skin elasticity and reduced wrinkle depth compared to conventional formulations, validated by AI-powered skin analysis technology.
著者： Liang Chen et al.
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.
著者： Fudi Wang et al.
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.
著者： Fudi Wang et al.
Research Square
著者： Jingjing Xia et al.