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 moisture contents.
RESULTS: A regression-type AI model using only a visible-light skin-containing image was insufficiently implemented. To improve the accuracy of the prediction of skin moisture content, we searched for new features through feature engineering ("creation of new factors") correlated with the moisture content from various combinations of the existing skin features, and have found that factors created by combining the brown spot count, the pore count, and/or the visually assessed skin roughness give significant correlation coefficients. Then, an integrated AI deep-learning model using a visible-light skin image and these factors resulted in significantly improved skin moisture content prediction.
CONCLUSION: Skin moisture content interacts with the brown spot count, the pore count, and/or the visually assessed skin roughness so that better inference of stratum corneum moisture content can be provided using a common visible-light skin photo image and skin feature factors.
