Point-Based Infrared Thermography and Two-Phase Machine Learning for Non-Invasive Periodontal Disease Classification
DOI:
https://doi.org/10.17488/RMIB.47.2.1666Keywords:
Infrared thermography, Machine learning, Periodontal disease, Non-invasive diagnosis, Medical imagingAbstract
Periodontal diseases such as gingivitis and periodontitis are common oral health conditions that require timely and accurate detection. This study presents a non-invasive diagnostic support system that integrates infrared thermography with clinical data to classify periodontal health status. A cross-sectional study was conducted on 91 individuals, categorized as healthy, with gingivitis, or with periodontitis. Thermographic images from three facial perspectives were analyzed to extract gingival temperature features, which were combined with clinical parameters including plaque index, age, sex, smoking status, and the presence of systemic conditions. Several machine learning models were evaluated using ten-fold cross-validation, both with and without dimensionality reduction. A two-step classification approach yielded the best results: logistic regression was used to identify periodontitis, followed by XGBoost to differentiate between healthy and gingivitis cases. The combined model achieved an accuracy of 94.51% and an F1-score of 94.49%, while models based solely on thermographic data reached an accuracy of 75.82%. These findings support the feasibility of using localized infrared thermographic measurements and artificial intelligence to improve the classification of periodontal disease. The proposed method offers a promising non-invasive solution to enhance diagnostic accuracy and inform personalized dental care.
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