Assessment of skin barrier function using skin images with topological data analysis

• Published in: NPJ systems biology and applications Vol. 6; no. 1; pp. 40 - 9
• Main Authors: Koseki, Keita, Kawasaki, Hiroshi, Atsugi, Toru, Nakanishi, Miki, Mizuno, Makoto, Naru, Eiji, Ebihara, Tamotsu, Amagai, Masayuki, Kawakami, Eiryo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.12.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1041; 639/705/1042; 692/699; 692/700; Adult; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data Analysis; Female; Humans; Image processing; Image Processing, Computer-Assisted - methods; Learning algorithms; Life Sciences; Machine Learning; Male; Phenotypes; Precision medicine; Skin - diagnostic imaging; Skin diseases; Skin Physiological Phenomena; Systems Biology; Water Loss, Insensible - physiology
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-020-00160-8

Recent developments of molecular biology have revealed diverse mechanisms of skin diseases, and precision medicine considering these mechanisms requires the frequent objective evaluation of skin phenotypes. Transepidermal water loss (TEWL) is commonly used for evaluating skin barrier function; however, direct measurement of TEWL is time-consuming and is not convenient for daily clinical practice. Here, we propose a new skin barrier assessment method using skin images with topological data analysis (TDA). TDA enabled efficient identification of structural features from a skin image taken by a microscope. These features reflected the regularity of the skin texture. We found a significant correlation between the topological features and TEWL. Moreover, using the features as input, we trained machine-learning models to predict TEWL and obtained good accuracy ( R 2  = 0.524). Our results suggest that assessment of skin barrier function by topological image analysis is promising.