An Exploratory Study Investigating the Intraoral Thermal Features in Advanced Periodontitis Using Machine Learning Techniques

Background/Objective: Periodontitis is a chronic inflammatory disease requiring reliable assessment of periodontal tissue involvement. This study investigates quantitative intraoral thermal features correlating with periodontal conditions using machine learning algorithms. Materials and Methods: The study included 108 participants, comprising 54 healthy controls and 54 participants with advanced periodontitis (Stage III/IV combined). Five intraoral thermal views were obtained from each participant, yielding 540 thermal images. Quantitative features describing temperature distribution, variation, and differences between anatomical views were extracted. Eleven machine-learning classifiers were evaluated using participant-level nested cross-validation, with hyperparameter tuning performed in the inner folds and performance assessed in the outer folds. Results: Higher thermal measurements were observed across all five intraoral views in the periodontitis group, accompanied by differences in PPD, CAL, and SBI. Among the evaluated classifiers, AdaBoost showed the highest internal validation performance, with an ROC-AUC of 0.998 (95% CI: 0.992–1.00), sensitivity of 98.15%, specificity of 98.15%, and accuracy of 98.15%. The extracted thermal features provided useful discriminatory information between the two groups. Conclusion: Quantitative intraoral thermal features showed potential for the exploratory screening of advanced periodontitis by distinguishing affected individuals from periodontally healthy controls.