Artificial Intelligence-Based Prototype for Early Diagnosis of Gingivitis and Periodontitis in Adults

Abstract

Background: Periodontal diseases continue to be highly prevalent worldwide, and their early detection represents a clinical challenge, especially when based on non-standardized intraoral photographs. The present study develops an artificial intelligence-based prototype for the automatic classification of periodontal health, gingivitis, and periodontitis using Red, Green, Blue (RGB) images obtained in real conditions. Methods: A dataset comprising 1552 (306 healthy, 1019 with gingivitis, and 227 with periodontitis) was constructed by integrating proprietary clinical photographs with a public repository. A patient-level stratified split was enforced to prevent data leakage, ensuring that all images from the same patient remained within a single partition. This proposal uses EfficientNet-B2, which includes two-phase training, balanced focal loss, weighted sampling, CutMix/MixUp augmentation, and centered anatomical cropping to improve generalization across varied images. Results: The final model achieved an accuracy of 0.833, a macro F1-score (F1) of 0.832 [95% CI: 0.789–0.874], and a macro Area Under the Curve (AUC) of 0.962 [95% CI: 0.946–0.976] on an independent test set. A seven-configuration ablation study showed that each training component contributes to improved performance, and a baseline comparison with ResNet-50 demonstrated the superiority of EfficientNet-B2. Five-fold cross-validation with patient-level grouping yielded consistent results (F1 = 0.832 ± 0.016, AUC = 0.950 ± 0.005). Conclusions: These results demonstrate that EfficientNet-B2 is useful for assessing periodontal health using readily available RGB photographs, with potential for early detection, clinical triage, and remote assessment in modern dentistry.