RÉSUMÉ
Objective The aim of this study is to combine Raman spectroscopy and machine learning techniques to distinguish subgin-gival plaques among three groups of subjects,including patients with chronic periodontitis(CP)and type 2 diabetes mellitus(T2DM),patients with CP alone,and healthy controls.Methods The Raman spectra of the subgingival plaques from 20 patients with CP and T2DM(group A),23 patients with CP alone(group B),and 23 healthy controls(group C)were obtained using a portable Raman spec-trometer.Eight common machine learning algorithms were applied to build models to distinguish the Raman spectra of the three types of subgingival plaques.Results The model identified as optimal for distinguishing the three types of subgingival plaques was linear discri-minant analysis(LDA).The optimal model to distinguish groups A and B is LDA,groups A and C is extra trees(ET),and groups B and C group is LDA.Conclusion The proposed classification model based on Raman spectroscopy and machine learning algorithms can dis-tinguish subgingival plaques among patients with CP and T2DM,with CP alone,and healthy controls.This technique can be used in future clinical practice as a screening or diagnostic tool.