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 ORCID: 0000-0002-5157-4042, 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).
034001-034001.
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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: | 18 Jan 2023 19:48 |
| DOI: | 10.1088/2633-1357/ac95e2 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3165824 |
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