Prediction of malignant transformation in oral epithelial dysplasia using machine learning.



Ingham, James, Smith, Caroline I ORCID: 0000-0001-6878-0697, Ellis, Barnaby G, Whitley, Conor A, Triantafyllou, Asterios, Gunning, Philip J, Barrett, Steve D ORCID: 0000-0003-2960-3334, Gardener, Peter, Shaw, Richard J, Risk, Janet M ORCID: 0000-0002-8770-7783
et al (show 1 more authors) (2022) Prediction of malignant transformation in oral epithelial dysplasia using machine learning. IOP SciNotes, 3 (3). 034001-.

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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.

Item Type: Article
Uncontrolled Keywords: FTIR spectroscopy, OED, machine learning, oral cancer
Divisions: Faculty of Health and Life Sciences
Faculty of Science and Engineering > School of Physical Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 28 Oct 2022 09:35
Last Modified: 28 Oct 2022 09:35
DOI: 10.1088/2633-1357/ac95e2
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3165824