Three-dimensional (3D) printing is increasingly being utilized in the dental field. After fabricating a prosthesis using a 3D printed resin, a post-curing process is required to improve its mechanical properties, but there has been insufficient research on the optimal post-curing conditions. We used various 3D printed crown and bridge materials in this study, and evaluated the changes in their properties according to post-curing time by evaluating the flexural strength, Weibull modulus, Vickers hardness, color change, degree of conversion, and biocompatibility. The obtained results confirmed that the strength of the 3D printed resin increased when it was post-cured for 60–90 min. The Vickers hardness, the degree of conversion, and biocompatibility of the 3D printed resins increased significantly around the beginning of the post-curing time, and then increased more gradually as the post-curing time increased further. It was observed that the color tone also changed as the post-curing time increased, with some groups showing a ΔE₀₀ value of ≥ 2.25, which can be recognized clinically. This study has confirmed that, after the printing process of a 3D printed resin was completed, a sufficient post-curing time of at least 60 min is required to improve the overall clinical performance of the produced material.

Three-dimensional (3D) printing is an attractive technology in dentistry. Acrylic-based 3D printed resin parts have to undergo postcuring processes to enhance their mechanical and biological properties, such as UV-light and thermal polymerization. However, no previous studies have revealed how the postcuring temperature influences the biocompatibility of the produced parts. Therefore, we postprocessed 3D printed denture teeth resin under different postcuring temperatures (40, 60 and 80 °C) for different periods (15, 30, 60, 90 and 120 min), and evaluated their flexural properties, Vickers hardness, cell cytotoxicity, cell viability, and protein adsorption. In addition, confocal laser scanning was used to assess the condition of human gingival fibroblasts. It was found that increasing the postcuring temperature significantly improved the flexural strength and cell viability. The flexural strength and cell viability were 147.48 ± 5.82 MPa (mean ± standard deviation) and 89.51 ± 7.09%, respectively, in the group cured at 80 °C for 120 min, which were higher than the values in the 40 and 60 °C groups. The cell cytotoxicity increased in the 40 °C groups and for longer cultivation time. Confocal laser scanning revealed identifiable differences in the morphology of fibroblasts. This study has confirmed that the postcuring temperature influences the final mechanical and biological properties of 3D printed resin.

Recent advances in three-dimensional (3D) printing have introduced new materials that can be utilized for dental restorations. Nonetheless, there are limited studies on the color stability of restorations using 3D-printed crowns and bridge resins. Herein, the color stability of conventional computer-aided design/computer-aided manufacturing (CAD/CAM) blocks and 3D-printing resins was evaluated and assessed for their degrees of discoloration based on material type, colorant types (grape juice, coffee, curry, and distilled water (control group)), and storage duration (2, 7, and 30 days) in the colorants. Water sorption, solubility, and scanning electron microscope (SEM) analyses were conducted. A three-way ANOVA analysis showed that all three factors significantly affected the color change of the materials. Notably, the discoloration (ΔE₀₀) was significantly higher in all 3D printing resins (4.74–22.85 over the 30 days) than in CAD/CAM blocks (0.64–4.12 over the 30 days) following immersion in all colorants. 3D-printing resins showed color differences above the clinical limit (2.25) following storage for 7 days or longer in all experimental groups. Curry was the most prominent colorant, and discoloration increased in almost all groups as the storage duration increased. This study suggests that discoloration must be considered when using 3D printing resins for restorations.

Three-dimensional (3D) printing technology is highly regarded in the field of dentistry. Three-dimensional printed resin restorations must undergo a washing process to remove residual resin on the surface after they have been manufactured. However, the effect of the use of different washing solutions and washing times on the biocompatibility of the resulting resin restorations is unclear. Therefore, we prepared 3D-printed denture teeth and crown and bridge resin, and then washed them with two washing solutions (isopropyl alcohol and tripropylene glycol monomethyl ether) using different time points (3, 5, 10, 15, 30, 60, and 90 min). After this, the cell viability, cytotoxicity, and status of human gingival fibroblasts were evaluated using confocal laser scanning. We also analyzed the flexural strength, flexural modulus, and surface SEM imaging. Increasing the washing time increased the cell viability and decreased the cytotoxicity (p < 0.001). Confocal laser scanning showed distinct differences in the morphology and number of fibroblasts. Increasing the washing time did not significantly affect the flexural strength and surface, but the flexural modulus of the 90 min washing group was 1.01 ± 0.21 GPa (mean ± standard deviation), which was lower than that of all the other groups and decreased as the washing time increased. This study confirmed that the washing time affected the biocompatibility and mechanical properties of 3D printed dental resins.

In the absence of accurate medical records, it is critical to correctly classify implant fixture systems using periapical radiographs to provide accurate diagnoses and treatments to patients or to respond to complications. The purpose of this study was to evaluate whether deep neural networks can identify four different types of implants on intraoral radiographs. In this study, images of 801 patients who underwent periapical radiographs between 2005 and 2019 at Yonsei University Dental Hospital were used. Images containing the following four types of implants were selected: Brånemark Mk TiUnite, Dentium Implantium, Straumann Bone Level, and Straumann Tissue Level. SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50 were tested to determine the optimal pre-trained network architecture. The accuracy, precision, recall, and F1 score were calculated for each network using a confusion matrix. All five models showed a test accuracy exceeding 90%. SqueezeNet and MobileNet-v2, which are small networks with less than four million parameters, showed an accuracy of approximately 96% and 97%, respectively. The results of this study confirmed that convolutional neural networks can classify the four implant fixtures with high accuracy even with a relatively small network and a small number of images. This may solve the inconveniences associated with unnecessary treatments and medical expenses caused by lack of knowledge about the exact type of implant.