Ensemble learning for predicting periapical lesions from dental radiographs.
Authors
Affiliations (3)
Affiliations (3)
- Department of Endodontics, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi Christian Hospital, Chia-Yi, Taiwan.
- Department of Information management, National Formosa University, Yulin, Taiwan.
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
Abstract
Diagnosing periapical cysts and granulomas using periapical radiographs is challenging due to subtle radiographic differences. This study aimed to develop a machine learning-based diagnostic framework to improve lesion classification through ensemble learning and dentin standardization. Five models-Support Vector Classifier (SVC), Nu-SVC, K-Nearest Neighbors (KNN) and Decision Tree-were trained on 144 pre-treatment periapical radiographs (70 cysts and 74 granulomas). Dentin standardization normalized grayscale values to ensure feature consistency. A weighted soft voting strategy was employed to integrate predictions, with model weights derived from individual diagnostic performance. Model performance was evaluated via five-fold cross-validation. Statistical significance among models was assessed using the Friedman test, followed by Wilcoxon signed-rank tests for pairwise comparisons (α = 0.05). The ensemble method achieved a precision of 0.83 and a sensitivity of 0.83, demonstrating robust diagnostic performance. Statistical analysis revealed significant differences among models in specificity (<i>P</i> = 0.001) and negative predictive value (<i>P</i> = 0.044), with the ensemble method reaching a peak specificity of 0.83. Although numerous improvements were observed in precision and sensitivity compared to several base models, these differences did not reach statistical significance (<i>P</i> > 0.05). Overall, the ensemble method demonstrated balanced performance across metrics, although its improvements over individual models were not statistically significant. By integrating dentin standardization with a weighted ensemble approach, the proposed method provides a reliable, non-invasive tool for improving radiographic differentiation of periapical cysts and granulomas. These results support the potential of intelligent diagnostic systems in dental radiology.