Classification of pit and fissure for caries risk based on 3D surface morphology analysis of tooth

Abstract

Tooth surface with pits and fissures is the most prevalent of carious area for suitability of plaque accumulation. Pit and fissure sealing has been proven be effective in preventing and arresting pit-and-fissure occlusal caries lesions of primary and permanent molars in children and adolescents and can greatly affect smooth surface carious lesion reduction. Clinical decision to seal enamel pits and fissures needs to assess caries risk of the tooth. Surface morphology of pit and fissure, judged by dentist’s subjective experience, together with other factors of socioeconomic status of family, dietary habit, caries history, etc, are comprehensively considered. Due to morphological complexity and diversity of tooth surface, the decision lacks objective morphology-based caries-risk assessment of pit and fissure. In the paper, dental plaque-guided evaluation of pit and fissure caries risk based on 3D morphology analysis of occlusal surface is investigated. The 3D point cloud data of tooth surface are obtained from a commercial 3D intra-oral scanner. Pit-andfissure region can be extracted using region growing. Then skeleton of pit and fissure is determined by L1-medial skeleton method. Section profile of pit-and-fissure can then be obtained for morphological analysis. Bearing area curve (BAC) is introduced to evaluate the morphological distribution and five BAC-based parameters are defined as quantitative indices to describe the characteristic of pit-and-fissure morphology. Dental plaque was quantitatively evaluated by image component ratio of fluorescence image. To obtain dental plaque distribution of 3D pit and fissure region, ICP-based contour registration method was proposed to map fluorescence image on 3D occlusal surface. Nonlinear modeling of plaque distribution and morphological feature was explored using RBF neural network. The reported work reveals that 3D morphological parameters can be used as effective predictors for pit and fissure caries risk evaluation.