Prediction of malignant transformation in oral epithelial dysplasia using machine learning

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 e...

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Autores principales: Ingham, James, Smith, Caroline I, Ellis, Barnaby G, Whitley, Conor A, Triantafyllou, Asterios, Gunning, Philip J, Barrett, Steve D, Gardener, Peter, Shaw, Richard J, Risk, Janet M, Weightman, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580266/
https://www.ncbi.nlm.nih.gov/pubmed/36277682
http://dx.doi.org/10.1088/2633-1357/ac95e2
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author Ingham, James
Smith, Caroline I
Ellis, Barnaby G
Whitley, Conor A
Triantafyllou, Asterios
Gunning, Philip J
Barrett, Steve D
Gardener, Peter
Shaw, Richard J
Risk, Janet M
Weightman, Peter
author_facet Ingham, James
Smith, Caroline I
Ellis, Barnaby G
Whitley, Conor A
Triantafyllou, Asterios
Gunning, Philip J
Barrett, Steve D
Gardener, Peter
Shaw, Richard J
Risk, Janet M
Weightman, Peter
author_sort Ingham, James
collection PubMed
description 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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spelling pubmed-95802662022-10-20 Prediction of malignant transformation in oral epithelial dysplasia using machine learning Ingham, James Smith, Caroline I Ellis, Barnaby G Whitley, Conor A Triantafyllou, Asterios Gunning, Philip J Barrett, Steve D Gardener, Peter Shaw, Richard J Risk, Janet M Weightman, Peter IOP SciNotes Article 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. IOP Publishing 2022-09-01 2022-10-07 /pmc/articles/PMC9580266/ /pubmed/36277682 http://dx.doi.org/10.1088/2633-1357/ac95e2 Text en © 2022 The Author(s). Published by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Article
Ingham, James
Smith, Caroline I
Ellis, Barnaby G
Whitley, Conor A
Triantafyllou, Asterios
Gunning, Philip J
Barrett, Steve D
Gardener, Peter
Shaw, Richard J
Risk, Janet M
Weightman, Peter
Prediction of malignant transformation in oral epithelial dysplasia using machine learning
title Prediction of malignant transformation in oral epithelial dysplasia using machine learning
title_full Prediction of malignant transformation in oral epithelial dysplasia using machine learning
title_fullStr Prediction of malignant transformation in oral epithelial dysplasia using machine learning
title_full_unstemmed Prediction of malignant transformation in oral epithelial dysplasia using machine learning
title_short Prediction of malignant transformation in oral epithelial dysplasia using machine learning
title_sort prediction of malignant transformation in oral epithelial dysplasia using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580266/
https://www.ncbi.nlm.nih.gov/pubmed/36277682
http://dx.doi.org/10.1088/2633-1357/ac95e2
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