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

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; howev...

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Published inNPJ 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
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
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.
Abstract 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.
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.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.
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  = 0.524). Our results suggest that assessment of skin barrier function by topological image analysis is promising.
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.
ArticleNumber 40
Author Nakanishi, Miki
Kawakami, Eiryo
Ebihara, Tamotsu
Kawasaki, Hiroshi
Koseki, Keita
Mizuno, Makoto
Naru, Eiji
Atsugi, Toru
Amagai, Masayuki
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Snippet Recent developments of molecular biology have revealed diverse mechanisms of skin diseases, and precision medicine considering these mechanisms requires the...
Abstract Recent developments of molecular biology have revealed diverse mechanisms of skin diseases, and precision medicine considering these mechanisms...
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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
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Title Assessment of skin barrier function using skin images with topological data analysis
URI https://link.springer.com/article/10.1038/s41540-020-00160-8
https://www.ncbi.nlm.nih.gov/pubmed/33339832
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