Authors:
Luiz Guilherme Kasputis Zanini
1
;
Izabel Regina Rubira-Bullen
2
and
Fátima de Lourdes dos Santos Nunes
3
Affiliations:
1
Department of Computer Engineering and Digital Systems, University of São Paulo, São Paulo, Brazil
;
2
Department of Surgery, Stomatology Pathology and Radiology, University of São Paulo, Bauru, Brazil
;
3
School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil
Keyword(s):
Image Processing, Machine Learning, Dental Caries, Segmentation, Classification, CBCT, ICDAS.
Abstract:
Dental caries are caused by bacterial action that demineralizes tooth enamel and dentin. It is a serious threat to oral health and potentially leads to inflammation and tooth loss if not adequately treated. Cone Beam Computed Tomography (CBCT), a three-dimensional (3D) imaging technique used in dental diagnosis and surgical planning, can potentially contribute to detection of caries. This study aims at developing a computational method to segment and classify caries in CBCT images. The process involves data preparation, segmentation of caries regions, extraction of relevant features, feature selection, and training machine learning algorithms. We evaluated our method performance considering different stages of caries severity based on the International Caries Detection and Assessment System scale. The best results were achieved using a Gaussian filter with a multimodal threshold with a convex hull for the region of interest segmentation, feature selection via Random Forest, and class
ification using a model based on k-nearest neighbors algorithm. We achieved outcomes with an accuracy of 86.20%, a F1-score of 86.18%, and a sensitivity of 83.35% in multiclass classification. These results show that our approach contributes to the early segmentation and classification of dental caries, thereby improving oral health outcomes and treatment planning.
(More)