Artificial intelligence model substantially improves stratum corneum moisture content prediction from visible-light skin images and skin feature factors.

ABSTRACT:

Background

Appropriate skin treatment and care warrants an accurate prediction of skin moisture. However, current diagnostic tools are costly and time-consuming. Stratum corneum moisture content has been measured with moisture content meters or from a near-infrared image.

Objective

Here, we establish an artificial intelligence (AI) alternative for conventional skin moisture content measurements.

Methods

Skin feature factors positively or negatively correlated with the skin moisture content were created and selected by using the PolynomialFeatures(3) of scikit-learn. Then, an integrated AI model using, as inputs, a visible-light skin image and the skin feature factors were trained with 914 skin images, the corresponding skin feature factors, and the corresponding skin

SUBMITTER: Shishido T 

PROVIDER: S-EPMC10363786 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature