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Lookup NU author(s): Professor Philip Preshaw
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© 2025Objective: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation efforts exist in Asia, where periodontitis is highly prevalent. Moreover, the diagnostic performance of individual self-reported measures and the relevant sociodemographic factors differs across populations. This study aims to clinically validate self-reported periodontal measures in a multiethnic community-based population and to develop several predictive models that combine sociodemographic characteristics and self-reported periodontal measures. Methods: We analysed cross-sectional data from 426 diabetes-free participants in Singapore who completed the Centers for Disease Control and Prevention/American Academy of Periodontology (CDC-AAP) self-reported periodontal questionnaire and underwent full-mouth periodontal examinations. Periodontitis status was defined using the 2012 CDC-AAP case definitions. Multivariable logistic regressions and area under the curve (AUC) analyses evaluated predictive models for periodontitis. Additionally, we performed an exploratory analysis, training and testing five machine learning models to predict periodontitis. Results: Participants had a mean age of 48.9±9.9 years, 55.6% were female, and 16.4%, 42.5%, and 18.1% had mild, moderate, and severe periodontitis, respectively. A multivariable model incorporating one self-reported question (loose teeth), age, and ethnicity demonstrated good discrimination for severe periodontitis (AUC=0.76; sensitivity/specificity=0.60/0.80). While the machine learning models achieved similar AUC (0.67-0.76), they tended to be highly specific (0.99-0.75) but had much lower sensitivity (0.12-0.63). Conclusion: Selected self-reported periodontitis questions were useful for detecting severe periodontitis in this population. While machine learning models integrated sociodemographic factors with all 8 self-reported questions, larger and more diverse datasets are needed to enhance model robustness and generalisability across international populations. Clinical significance: Self-reported periodontal measures validated in a multiethnic population demonstrated good predictive value for severe periodontitis when combining self-report of loose teeth with demographic variables age, gender, and ethnicity. The application of machine learning models further enhanced diagnostic performance, highlighting potential for scalable, data-driven periodontal screening and surveillance across diverse populations.
Author(s): Goh CE, CHEW JRJ, Lai C, Seah F, Lim M, Febriana E, Lee MH, Feng JKT, Tan KS, Fu J, Yip JK, Toh S-A, Preshaw PM
Publication type: Article
Publication status: Published
Journal: Journal of Dentistry
Year: 2026
Volume: 165
Print publication date: 01/02/2026
Online publication date: 08/12/2025
Acceptance date: 08/12/2025
ISSN (print): 0300-5712
ISSN (electronic): 1879-176X
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.jdent.2025.106294
DOI: 10.1016/j.jdent.2025.106294
PubMed id: 41371537
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