James Ingham, Caroline I Smith, Barnaby G Ellis, Conor A Whitley, Asterios Triantafyllou, Philip J Gunning, Steve D Barrett, Peter Gardener, Richard J Shaw, Janet M Risk, Peter Weightman
{"title":"Prediction of malignant transformation in oral epithelial dysplasia using machine learning.","authors":"James Ingham, Caroline I Smith, Barnaby G Ellis, Conor A Whitley, Asterios Triantafyllou, Philip J Gunning, Steve D Barrett, Peter Gardener, Richard J Shaw, Janet M Risk, Peter Weightman","doi":"10.1088/2633-1357/ac95e2","DOIUrl":null,"url":null,"abstract":"<p><p>A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.</p>","PeriodicalId":93771,"journal":{"name":"IOP SciNotes","volume":"3 3","pages":"034001"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580266/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IOP SciNotes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2633-1357/ac95e2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/10/7 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.
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