Dataset Information

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

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 (R² = 0.524). Our results suggest that assessment of skin barrier function by topological image analysis is promising.

SUBMITTER: Koseki K

PROVIDER: S-EPMC7749164 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Koseki Keita K Kawasaki Hiroshi H Atsugi Toru T Nakanishi Miki M Mizuno Makoto M Naru Eiji E Ebihara Tamotsu T Amagai Masayuki M Kawakami Eiryo E

NPJ systems biology and applications 20201218 1

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 analys ...[more]

PMID: 33339832

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Project description:BackgroundOsteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques.PurposeTo identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA.Study typeLongitudinal study for comparison of progressive and nonprogressive subjects.PopulationIn all, 102 subjects with and without radiographic signs of hip osteoarthritis.Field strength/sequence3T, SPGR 3D MAPSS T1ρ /T2 , intermediate-weighted fat-suppressed fast spin-echo (FSE).AssessmentMultidimensional data analysis including cartilage composition, bone shape, Kellgren-Lawrence (KL) classification of osteoarthritis, scoring hip osteoarthritis with MRI (SHOMRI), hip disability and osteoarthritis outcome score (HOOS).Statistical testsAnalysis done using TDA, Kolmogorov-Smirnov (KS) testing, and Benjamini-Hochberg to rank P-value results to correct for multiple comparisons.ResultsSubjects in the later stages of the disease had an increased SHOMRI score (P < 0.0001), increased KL (P = 0.0012), and older age (P < 0.0001). Subjects in the healthier group showed intact cartilage and less pain. Subjects found between these two groups had a range of symptoms. Analysis of this subgroup identified knee biomechanics (P < 0.0001) as an initial marker of the disease that is noticeable before the morphological progression and degeneration. Further analysis of an OA subgroup with femoroacetabular impingement (FAI) showed anterior labral tears to be the most significant marker (P = 0.0017) between those FAI subjects with and without OA symptoms.Data conclusionThe data-driven analysis obtained with TDA proposes new phenotypes of these subjects that partially overlap with the radiographic-based classical disease status classification and also shows the potential for further examination of an early onset biomechanical intervention.Level of evidence2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058.

Dataset Information

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

Publications

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

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