Investigation of stratum corneum cell morphology and content using novel machine-learning image analysis.

ABSTRACT:

Background

The morphology and content of stratum corneum (SC) cells provide information on the physiological condition of the skin. Although the morphological and biochemical properties of the SC are known, no method is available to fully access and interpret this information. This study aimed to develop a method to comprehensively decode the physiological information of the skin, based on the SC. Therefore, we established a novel image analysis technique based on artificial intelligence (AI) and multivariate analysis to predict skin conditions.

Materials and methods

SC samples were collected from participants, imaged, and annotated. Nine biomarkers were measured in the samples using enzyme-linked immunosorbent assay. The data were then used to teach machine-learning models

SUBMITTER: Tohgasaki T 

PROVIDER: S-EPMC10818130 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature