A Clinically Interpretable Machine Learning Model for Staging and Grading of Periodontitis: Development and Temporal External Validation as a Decision Support Tool

Background The 2018 international classification for periodontitis enables individualized patient management through simultaneous staging (disease severity and complexity) and grading (progression rate). However, its clinical adoption is hindered by diagnostic inconsistency—particularly in grading and differentiation of advanced stages—due to the complexity of age-adjusted metrics such as radiographic bone loss–to–age ratio (RBL/age). While artificial intelligence (AI) shows promise in dental diagnostics, existing tools lack robust integration of multimodal clinical data and validation under real-world conditions. Methods We retrospectively collected data from 692 patients diagnosed with periodontitis at Hospital of Stomatology, Guangxi Medical University between June 2022 and June 2024. After data cleaning and feature selection, cases were labeled according to the 2018 international classification criteria. Machine learning models—including k-nearest neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—were trained and optimized via hyperparameter tuning. Model performance was evaluated on internal test sets and further validated on a temporally held-out external cohort (n = 208). Results For staging, KNN, RF, and DT achieved 96.99% accuracy; DT showed the highest recall (94.74%) and F1-score (0.9231) for Stage II, while RF excelled in Stage III (recall and F1-score: 0.9747). For grading, KNN and RF reached 96.40% accuracy, with F1-scores > 0.97 for Grade C. Feature importance analysis identified clinical attachment loss (CAL) and probing depth (PD) as top predictors for staging, and RBL/age as the dominant feature for grading. In temporal external validation, the model achieved 93.75% accuracy for full diagnosis (extent + stage + grade). Conclusions The proposed model demonstrates high accuracy, interpretability, and generalizability, offering a promising decision-support tool for standardized periodontitis classification.