Effect of Different Sintering Procedures on Marginal and Internal Fit, Color, and Fracture Load of Monolithic Zirconia Fixed Partial Prosthesis.

AIM: The aim of this in vitro study is to investigate the effects of different sintering procedures on the fit, color parameters, and fracture load of monolithic fixed partial prosthesis. MATERIALS AND METHODS: Metal model was scanned and fixed partial prosthesis was designed. Groups were created by fabricating fixed partial prosthesis by using four different sinterization procedures (Prettau-Standard (PST), Prettau-Slow (PSL), Ice-Speed (IS), Ice-Standard (IST), n=10). PST-PSL (Group P, N=20) and IS-IST (Group I, N=20) were colored with different coloring liquids. The marginal and internal fit were measured using the silicone replica method. CIELAB values of the samples were measured using a spectrophotometer. Then, for each sample, the die was obtained from polymethyl methacrylate. The specimens were cemented into dies and tested in a universal testing machine for fracture load. One-way ANOVA were performed to assess the effect of the sintering procedure on the marginal and internal fit, fracture load, and ∆E00, ∆L', ∆C', and ∆H' values of fixed partial prosthesis. RESULTS: PSL and PST groups showed significantly smaller internal and marginal fit compared to the IS group. Additionally, IST group internal fit values were significantly higher than Prettau groups. Sintering time reduction led to a decrease in ∆E00 values. Fracture loads values were not statistically significantly affected by the different sintering procedures in both brands. CONCLUSION: Different sintering procedures did not have a clinically significant effect on fit and fracture load. Different sintering procedures were found to have an impact on the color change of monolithic zirconia restorations.

Recycling selective laser melting alloy powder on cobalt chromium-to-ceramic bond strength.

STATEMENT OF PROBLEM: Reusing the powder in selective laser melting machines after multiple cycles is a cost-effective procedure for dental laboratories. However, information on the metal-ceramic bond strength of the framework fabricated by using recycled powder is lacking. PURPOSE: The purpose of this in vitro study was to investigate how the bonding agent and repeated alloy powder reuse affected the metal-ceramic bond strength of cobalt chromium frameworks fabricated by using selective laser melting. MATERIAL AND METHODS: Four square and 40-bar-shaped cobalt chromium frameworks were fabricated by selective laser melting. Half were produced by using virgin alloy powder (Group V; nsquare=2, nbar=20), and half with 30-times reused powder (Group R; nsquare=2, nbar=20). The particle size of each powder was measured by using scanning electron microscopy, and its phase composition was characterized by using radiograph diffraction. Each group was divided into 2 subgroups (Group W [Wash Opaque] and Group N [NP-Bond]) according to the brand of bonding agent used. After ceramic application, the metal-ceramic bond strengths were evaluated by using 3-point bend tests. The bonding agents' chemical composition was analyzed by using radiograph fluorescence. Bond strength data were analyzed by using a 2-way analysis of variance (α=.05). RESULTS: Mean ±standard deviation bond strengths did not differ significantly (P>.05) between Groups V (31.25 ±4.65) and R (30.88 ±4.78). Group W (35.34 ±1.78) had significantly higher bond strength than Group N (26.80 ±1.74; P<.001). Radiograph diffraction analysis found that the phase composition of all powders was similar. The bonding agent in Group W contained cerium, whereas, that in Group N did not. CONCLUSIONS: Metal-ceramic bond strength was unaffected by alloy powder reuse. However, the bonding agent brand may affect the bond strength of cobalt chromium frameworks fabricated by using selective laser melting.

A robust deep learning model for the classification of dental implant brands.

OBJECTIVE: In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for the classification of dental implant systems using panoramic radiographs. The primary objective is to assess the superiority of the proposed model in achieving accurate and efficient dental implant classification. MATERIAL AND METHODS: A comprehensive analysis was conducted using a diverse set of 25 convolutional neural network (CNN) models, including popular architectures such as VGG16, ResNet-50, EfficientNet, and ConvNeXt. The dataset of 1258 panoramic radiographs from patients who underwent implant treatment at faculty of dentistry was utilized for training and evaluation. Six different dental implant systems were employed as prototypes for the classification task. The precision, recall, F1 score, and support scores for each class have included in the classification accuracy report to ensure accurate and reliable results from the model. RESULTS: The experimental results demonstrate that the proposed model consistently outperformed the other evaluated CNN architectures in terms of accuracy, precision, recall, and F1-score. With an impressive accuracy of 95.74 % and high precision and recall rates, the ConvNeXt model showcased its superiority in accurately classifying dental implant systems. Notably, the model's performance was achieved with a relatively smaller number of parameters, indicating its efficiency and speed during inference. CONCLUSION: The findings highlight the effectiveness of deep learning techniques, particularly the proposed model, in accurately classifying dental implant systems from panoramic radiographs.

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.