Precise assessment of facial skin based on multimodal data fusion of the microbiome

The skin microecology plays a vital role in maintaining cutaneous health and is intricately linked to host skin phenotypes. However, there remains a lack of precise and quantitative biomarkers for evaluating skin health conditions, making it challenging to identify individuals at potential risk of microecological imbalance. In this study, we collected facial skin microbiomes from 242 female volunteers aged 16-50 years in Shanghai, China. Microbial communities were surveyed using 16S rRNA gene sequencing and integrated with high-resolution facial images and host skin phenotypes for comprehensive analysis. Our findings reveal that although the community complexity of the facial microbiome is comparable between individuals with "ideal" and "non-ideal" skin conditions, the composition and structure of key microbes varied significantly between the two groups. Differential abundance analysis further emphasized the role of interactions between the skin microbiome and host phenotypic traits in skin aging and status transitions. To enable a more holistic and quantitative assessment of facial skin status, we developed a novel multimodal skin index (MSI) that fuses multiple modal data from facial images, microbiome profiles, and host skin phenotype features through a deep learning-based framework. Importantly, MSI identified individuals with an outwardly healthy facial appearance but significant underlying microbial dysbiosis that conventional diagnostic approaches often overlook. This work enables the detection of such hidden risks, offering new avenues for individualized facial skin health assessment, precision dermatology, and microbiome-informed esthetic interventions.