Prediction of malignant transformation in oral epithelial dysplasia using machine learning. (1st September 2022)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: IOP SciNotes. Volume 3:Number 3(2022)
• Publication Date: 2022-09-01
• Subjects: machine learning -- oral cancer -- OED -- FTIR spectroscopy; 500
• DOI: 10.1088/2633-1357/ac95e2
• Languages: English
• ISSNs: 2633-1357

Abstract

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.