Effects of three disinfection solutions on residual monomers released from resin nanoceramic CAD/CAM blocks.

The aim of this study was to evaluate the effects of three disinfection solutions on the amount of monomers released from resin nanoceramic CAD/CAM blocks using high performance liquid chromatography (HPLC). Forty resin nanoceramic CAD/CAM (Cerasmart, GC, Japan) samples (12x14 × 2 mm) were divided into four groups; each group was disinfected using one of four solutions (Group 1: no disinfectant; Group 2: 70 % ethanol; Group 3: 2 % glutaraldehyde; and Group 4: 1 % sodium hypochlorite) for 5 min. Analysis of residual monomers (UDMA and Bis-EMA) amounts was performed using an HPLC instrument (Dionex Ultimate 3000, Thermo Fisher Scientific). After 30 days, the amounts of monomers found were as follows: 14.54 ppm for Group 1; 9.28 ppm for Group 2; 10.60 ppm for Group 3; and 2.76 ppm for Group 4 (the smallest monomer amount) (p < 0.001). Disinfection of indirect restorations prior to cementation can reduce the amount of residual monomers remaining from resin nanoceramic CAD/CAM blocks.

Effect of repeated preheating on monomer elution from a bulk-fill composite resin.

Background: Due to incomplete polymerization of composite resin restorations, residual monomers adversely affect their mechanical properties and biocompatibility. Preheating of composite resins is advised to increase the degree of conversion and reduce monomer elution. This study aimed to analyze the effect of preheating and repeated preheating on the amount of monomer released from a bulk-fill composite resin. Methods: Forty samples were prepared using Filtek One Bulk Fill Restorative composite resin. Samples in one group were fabricated at room temperature, whereas the composite resins in the other groups were cured after 1, 10, or 20 repeated preheating cycles (55 °C), 10 in each group. Eluted urethane dimethacrylate (UDMA) and bisphenol-A-glycidylmethacrylate (BisGMA) monomers were measured with high-performance liquid chromatography (HPLC) 24 hours and 30 days after immersion. The data were evaluated using one-way ANOVA and post hoc Tukey tests. Paired-sample t tests were used to test the differences between time intervals. Results: At both time intervals, the greatest amounts of released BisGMA, UDMA, and total monomers were obtained from the control group, whereas 10 preheating cycles resulted in the least monomer elution. The decrease in monomer elution was not statistically significant after 10 preheating cycles compared with that after one preheating cycle (P>0.05). The group with 20 preheating cycles showed a greater amount of monomer elution compared to that with 1 and 10 cycles, which was statistically significant (P < 0.05). The amount of released monomers on day 30 was significantly higher than on day 1 (P<0.01). Conclusion: Preheating of the bulk-fill composite resin was shown to be effective in reducing monomer elution. However, monomer elution was adversely affected after repeated preheating cycles of 20.

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.

OBJECTIVES: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS: The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION: CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs.

OBJECTIVES: Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. METHODS: The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model. RESULTS: The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively. CONCLUSIONS: A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.