BMC oral health · 2026
Non-invasive periodontal screening using self-reported-oral-health (SROH) questionnaire and salivary biomarkers: development and validation of machine learning models.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Ho Jan Yang, Anuwar Ainol Haniza Kherul, Karen-Ng Lee Peng, Alias Abd Aziz, Zainuddin Siti Lailatul Akmar, Rahman Mohammad Tariqur, Mohamed Nazari Nor Shafina, Kamal Nurzaman Nur Falihin Soehairq, Baharuddin Nor Adinar
Аннотация
BACKGROUND: Accurate and accessible screening tools for periodontitis are essential for early detection and disease prevention. This study evaluated a non-invasive diagnostic approach integrating sociodemographic data, self-reported oral health (SROH) questionnaires, and salivary biomarkers, using both conventional statistical and machine learning (ML) predictive models.
METHODS: Seventy-seven adults completed a validated SROH questionnaire and provided saliva samples for quantification of six biomarkers: interleukins (IL-1β, IL-6), tumour necrosis factor (TNF-α), matrix metalloproteinases (MMP-8, MMP-9), and metallothionein (MT). Participants were clinically classified as having (i) periodontal health, (ii) gingivitis, or (iii) periodontitis. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) across three feature sets: (i) SROH, salivary biomarkers and sociodemographic, (ii) SROH and salivary biomarkers, (iii) SROH and sociodemographic and (v) SROH alone. Model performance was assessed using 10-fold cross-validation and standard evaluation metrics.
RESULTS: The RF model trained on SROH, and salivary biomarkers achieved the highest accuracy with area under the receiver operating characteristic curve (AUC = 0.856), with superior precision (70.13%), sensitivity (0.701) and lower error rates (RMSE = 0.371) compared with NB (AUC = 0.795) and LR (AUC = 0.724) models in detecting periodontitis.
CONCLUSIONS: This non-invasive, SROH and biomarker-integrated approach shows potential as a first-line screening and referral tool in primary care and population-based settings where comprehensive periodontal examination is not routinely available. Further validation in larger, more diverse populations is warranted to support clinical translation.
METHODS: Seventy-seven adults completed a validated SROH questionnaire and provided saliva samples for quantification of six biomarkers: interleukins (IL-1β, IL-6), tumour necrosis factor (TNF-α), matrix metalloproteinases (MMP-8, MMP-9), and metallothionein (MT). Participants were clinically classified as having (i) periodontal health, (ii) gingivitis, or (iii) periodontitis. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) across three feature sets: (i) SROH, salivary biomarkers and sociodemographic, (ii) SROH and salivary biomarkers, (iii) SROH and sociodemographic and (v) SROH alone. Model performance was assessed using 10-fold cross-validation and standard evaluation metrics.
RESULTS: The RF model trained on SROH, and salivary biomarkers achieved the highest accuracy with area under the receiver operating characteristic curve (AUC = 0.856), with superior precision (70.13%), sensitivity (0.701) and lower error rates (RMSE = 0.371) compared with NB (AUC = 0.795) and LR (AUC = 0.724) models in detecting periodontitis.
CONCLUSIONS: This non-invasive, SROH and biomarker-integrated approach shows potential as a first-line screening and referral tool in primary care and population-based settings where comprehensive periodontal examination is not routinely available. Further validation in larger, more diverse populations is warranted to support clinical translation.