Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study
Abstract
ABSTRACT Aim To construct predictive models of periodontitis progression by applying Machine Learning (ML) to baseline data from a study of periodontitis progression. Materials and Methods Logistic regression (LR), multi‐layer perceptron (MLP) and probabilistic graphic model (PGM) were utilised on data from a multi‐centre longitudinal study in which periodontally healthy (n = 113) and periodontitis participants (n = 302) were examined bi‐monthly for 12 months without treatment. Periodontal examination was performed, and salivary levels of 10 analytes were determined. Clinical and demographic parameters and analytes levels were input into the model. The performance of 14 models was compared using the area under the receiver operating characteristic curve (AUROC), and feature importance was assessed using SHapley Additive exPlanations (SHAP). Results The PGM model (Clinical measures, saliva IL‐1β, age, sex) demonstrated the best overall performance (AUROC = 0.88), compared to LR (AUROC = 0.72) and MLP (AUROC = 0.58). Although MLP had a lower Brier score (0.12), its sensitivity was 0, limiting its clinical utility. In contrast, PGM achieved a balanced sensitivity (0.55) and specificity (0.81). Feature importance analyses highlighted the number of deep periodontal pockets as a key driver of model predictions in both PGM and MLP. Conclusions ML models can predict periodontitis progression, supporting early detection strategies. Our integrative approach, combining clinical data…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 302 participants or records.
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