Background Cracked teeth may cause various clinical symptoms depending on the extension depth of the crack and the subsequent bacterial infections. However, techniques to reliably determine the extension depths of cracks in teeth before treatment are lacking. The aim of this study was to develop a new technique based on contrast-enhanced cone beam computed tomography (CBCT) to improve the accuracy of crack depth evaluation in vitro. Methods We developed an in vitro artificial simulation model of cracked teeth. Pre-experimental CBCT (pre-CBCT), and micro-computed tomography (micro-CT) were first performed for all cracked teeth (n = 31). Contrast-enhanced CBCT was then performed by infiltrating the crack with ioversol under vacuum conditions. The sensitivities of pre-CBCT and contrast-enhanced CBCT for the diagnosis of cracked teeth were calculated. According to the K-means clusters, crack depths measured by micro-CT were changed into categorical variables. Bland–Altman plot and the intraclass correlation coefficient (ICC) were used to analyze the consistency of the crack depths between the pre-CBCT and contrast-enhanced CBCT, as well as the ICC between the contrast-enhanced CBCT and micro-CT. Receiver operating characteristic (ROC) curves were generated to assess the ability for predicting crack depth in the differential diagnosis using pre-CBCT and contrast-enhanced CBCT. Restricted cubic splines were also used to model the non-linear relationship between the crack depths of contrast-enhanced CBCT and micro-CT. Results The sensitivities of pre-CBCT and contrast-enhanced CBCT were 48.4%, and 67.7%, respectively. The ICC value of crack depth as measured by pre-CBCT and contrast-enhanced CBCT was 0.847 (95% confidence interval [CI] 0.380–0.960; P < 0.001). The areas under ROC curves (AUC) of pre-CBCT and contrast-enhanced CBCT were different: the AUC of pre-CBCT was 0.958 (P = 0.000, 95% CI 0.843–1.074), and the AUC of contrast-enhanced CBCT was 0.979 (P = 0.000, 95% CI 0.921–1.037), and the difference was not statistically significant (Z = − 0.707, P = 0.480). The ICC value of crack depth as measured by contrast-enhanced CBCT and micro-CT was 0.753 (95% CI 0.248–0.911; P < 0.001). Conclusion Contrast-enhanced CBCT under vacuum conditions with a contrast medium can significantly improve the crack detection rate of cracked teeth; however, it cannot measure the crack depths accurately.
Background According to the diagnosis criteria of the American Association of Endodontists (AAE), sensitive responses to cold and/or heat tests of suspected teeth compared with those of control teeth can be used for the diagnosis of pulpitis, but the role of electric pulp test (EPT) is not mentioned. It is believed that EPT has some limitations in determining the vitality of the pulp. The aim of this study was to explore the association between the difference in EPT values and the differential diagnoses of reversible pulpitis (RP) and symptomatic irreversible pulpitis (SIRP) caused by dental caries. Methods A total of 203 cases with pulpitis caused by dental caries were included. A diagnosis of pulpitis was made on the basis of the diagnostic criteria of AAE. Patient demographic and clinical examination data were collected. The EPT values of the suspected teeth and control teeth were measured, and the differences between them were calculated. The correlation between the difference in the EPT values and diagnosis of pulpitis was analyzed using univariate and multivariate logistic regression. Results In the 203 cases (78 males and 125 females; 115 cases of RP, 88 cases of SIRP; 9 anterior teeth, 59 premolars, and 135 molars), the mean patient age was 34.04 ± 13.02 (standard deviation) years. The unadjusted (crude) model, model 1 (adjusted for age), model 2 (adjusted for age and sex), and model 3 (adjusted for age, sex, and tooth type) were established for the statistical analyses. In model 3 [odds ratio (OR) = 1.025; 95% confidence interval (CI) 1.002–1.050; P = 0.035], the difference in EPT values between RP and SIRP was statistically significant. However, the areas under the curve of predictive probability of the crude model, model 1, model 2, and model 3 were 0.565, 0.570, 0.585, and 0.617, respectively, showing that the model accuracy was low. The P-value for the trend in differences between the EPT values as a categorical variable showed that the differences in the EPT values, comparing RP and SIRP, were not statistically significant. Conclusions Based on the present data, the difference in EPT values was not sufficient to differentiate RP from SIRP.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.