TY - JOUR
T1 - Assessment of skin barrier function using skin images with topological data analysis
AU - Koseki, Keita
AU - Kawasaki, Hiroshi
AU - Atsugi, Toru
AU - Nakanishi, Miki
AU - Mizuno, Makoto
AU - Naru, Eiji
AU - Ebihara, Tamotsu
AU - Amagai, Masayuki
AU - Kawakami, Eiryo
N1 - Funding Information:
This work was partially supported by KOSÉ Corporation. T.A., M.N., M.M., and E.N. are employed by KOSÉ Corporation.
Funding Information:
This work was supported by RIKEN Hub for predictive and preventive precision medicine driven by big data in JST Support program for starting up innovation hub (ihub), SECOM Science and Technology Foundation (to E.K.) and AMED under grant numbers JP17gm5010003, JP19gk0110043 (to E.K.), JP18ek0410046, and JP19ek0410058 (to H.K., E.K., T.E., and M.A.).
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12
Y1 - 2020/12
N2 - 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 (R2 = 0.524). Our results suggest that assessment of skin barrier function by topological image analysis is promising.
AB - 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 (R2 = 0.524). Our results suggest that assessment of skin barrier function by topological image analysis is promising.
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U2 - 10.1038/s41540-020-00160-8
DO - 10.1038/s41540-020-00160-8
M3 - Article
C2 - 33339832
AN - SCOPUS:85097786911
SN - 2056-7189
VL - 6
JO - npj Systems Biology and Applications
JF - npj Systems Biology and Applications
IS - 1
M1 - 40
ER -