Please use this identifier to cite or link to this item: https://doi.org/10.1002/jbio.201600303
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dc.titleDevelopment of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy
dc.contributor.authorYan, Jie
dc.contributor.authorYu, Yang
dc.contributor.authorKang, Jeon Woong
dc.contributor.authorTam, Zhi Yang
dc.contributor.authorXu, Shuoyu
dc.contributor.authorFong Li Shan Eliza
dc.contributor.authorSingh, Surya Pratap
dc.contributor.authorSong, Ziwei
dc.contributor.authorLisa Tucker-Kellogg
dc.contributor.authorSo, Peter T. C.
dc.contributor.authorYu,Hanry
dc.date.accessioned2017-12-18T09:07:41Z
dc.date.available2017-12-18T09:07:41Z
dc.date.issued2017-12-01
dc.identifier.citationYan, Jie, Yu, Yang, Kang, Jeon Woong, Tam, Zhi Yang, Xu, Shuoyu, Fong Li Shan Eliza, Singh, Surya Pratap, Song, Ziwei, Lisa Tucker-Kellogg, So, Peter T. C., Yu,Hanry (2017-12-01). Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy. Journal of biophotonics 10 (12) : 1703-1713. ScholarBank@NUS Repository. https://doi.org/10.1002/jbio.201600303
dc.identifier.issn1864063X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/137961
dc.description.abstractNon-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries [1]. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85-0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.
dc.language.isoen
dc.publisherWiley-VCH Verlag
dc.subjectRaman micro-spectroscopic imaging
dc.subjectbiochemical component analysis
dc.subjectmodel fitting
dc.subjectnon-alcoholic fatty liver disease
dc.subjectnon-alcoholic steatohepatitis
dc.typeArticle
dc.contributor.departmentDEPT OF BIOMEDICAL ENGINEERING
dc.contributor.departmentDEPT OF PHYSIOLOGY
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.description.doi10.1002/jbio.201600303
dc.description.sourcetitleJournal of biophotonics
dc.description.volume10
dc.description.issue12
dc.description.page1703-1713
dc.identifier.isiut000417186600015
dc.published.statePublished
dc.grant.idNMRC (R-185-000-294-511)
dc.grant.id(R-714-001-003- 271)
dc.grant.fundingagencySingapore Ministry of Health’s National Medical Research Council
dc.grant.fundingagencyMechanobiology Institute of Singapore
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