Background This study aimed to compare the size and location of the traditional and conservative endodontic access cavities of the right maxillary first molar teeth, projected on the occlusal surface using cone-beam computed tomography (CBCT), to obtain an ideal access cavity. Material/Methods Five hundred CBCT images of the right maxillary first molars, including 198 males and 302 females, were retrospectively evaluated using KaVo eXam Vision software. First, a rectangular coordinate system was established. The coordinates of 4 pulp horns and 3 root canal orifices, which projected on the occlusal surface, were marked on it. Two different access cavities were then created by connecting these points: (1) traditional endodontic access cavity (TEC) required removal of the entire roof of the pulp chamber to establish a straight-line access to the root canal system; (2) conservative endodontic access cavity (CEC) was formed by connecting the projection of each root canal orifice on the occlusal. Data were analyzed using Kruskal-Wallis and Pearson’s correlation tests at a 5% significance level. Results The area of TEC was approximately 9.61 mm 2 for males and 8.91 mm 2 for females. The area of CEC was approximately 3.4 mm 2 for males and 3.16 mm 2 for females. The projections of all pulp horns and root canal orifices were in or near the central area of nine-rectangle-grid. Conclusions Compared with the traditional access cavity, creating a conservative access cavity was less invasive. Meanwhile, the access cavity should be limited to the central or near the central area of nine-rectangle-grid.
Background To establish a risk assessment of dental-maxillofacial morphology affecting alveolar bone loss in patients with periodontitis using machine learning algorithms.Methods Four machine learning algorithms were used to screen possible predictor variables such as age, sex, clinical probing depth (CPD), skeletal relationship between the maxilla and mandible (ANB angle), mandibular plane angle (FH-MP), upper and lower central incisor inclination (U1-L1). The algorithms were also used to establish a risk assessment model in patients with periodontitis. A receiver operating characteristic curve was used to evaluate the discrimination of the models. The model was visualized by a nomogram.Results The optimal variables screened were CPD and FH-MP using random forest algorithm; CPD, FH-MP and U1-L1 using lasso regression; and CPD, FH-MP, and age using both optimal subset regression and cross-validation. CPD, FH-MP, U1-L1, and age were selected as the optimal prediction subsets. The area under the receiver operating characteristic curve was 0.778.Conclusions Within the limitations of this study, FH-MP was an important predictor of the degree of alveolar bone loss in the first molar affected by periodontitis. The degree of alveolar bone loss in the first molar was more serious in high-angle periodontitis than in those with low- and average-angle periodontitis.
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