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Purpose: To measure the influence of postpolymerization condition (dry or submerged in water) and time (2, 10, 20, and 40 minutes) on the accuracy of additively manufactured model material. Materials and methods: A bar standard tessellation language file was used to manufacture the resin specimens (E-Model Light, EnvisionTEC) using a 3D printer (Vida HD, EnvisionTEC). Two groups were created based on the postpolymerization condition: dry (D group) or submerged in water (W group). Each group was divided into four subgroups (D1 to D4 and W1 to W4) depending on the postpolymerizing time (2, 10, 20, and 40 minutes; n = 20 each; N = 160). The specimen dimensions were measured using a low-force digital caliper (Absolute Low Force Caliper Series 573, Mitutoyo). The volume was calculated: V = l × w × h. Shapiro-Wilk test revealed that the data were not normally distributed. Data were analyzed using Kruskal-Wallis and pairwise Mann-Whitney U tests (α = .05). Results: Significant differences in length, width, height, and volume values were found among the subgroups (P < .0018). In all groups, the width dimension (x-axis) presented the worst accuracy compared to height (z-axis) and length (y-axis) (P < .0018). The D2 and D4 subgroups obtained the closest dimensions to the virtual design; additionally, no significant differences were found between the two subgroups (P < .0018). Dry condition showed higher manufacturing accuracy compared to the water-submerged condition. In the water-submerged subgroups, the highest accuracy was obtained in the W2 and W4 subgroups (P < .0018). Conclusion: Postpolymerization conditions and time influenced the accuracy of the material tested. Dry postpolymerization condition with a time of 10 and 40 minutes obtained the highest accuracy.

Purpose: To compare the performance of licensed dentists and two software versions (3.5 legacy and 4.0) of an artificial intelligence (AI)-based chatbot (ChatGPT) answering the exam for the 2022 Certification in Implant Dentistry of the European Association for Osseointegration (EAO). Materials and Methods: The 50-question, multiple-choice exam of the EAO for the 2022 Certification in Implant Dentistry was obtained. Three groups were created based on the individual or program answering the exam: licensed dentists (D group) and two software versions of an artificial intelligence (AI)-based chatbot (ChatGPT)—3.5 legacy (ChatGPT-3.5 group) and the 4.0 version (ChatGPT-4.0 group). The EAO provided the results of the 2022 examinees (D group). For the ChatGPT groups, the 50 multiple-choice questions were introduced into both ChatGBT versions, and the answers were recorded. Pearson correlation matrix was used to analyze the linear relationship among the subgroups. The inter- and intraoperator reliability was calculated using Cronbach’s alpha coefficient. One-way ANOVA and Tukey post-hoc tests were used to examine the data (α = .05). Results: ChatGPT was able to pass the exam for the 2022 Certification in Implant Dentistry of the EAO. Additionally, the software version of ChatGPT impacted the score obtained. The 4.0 version not only pass the exam but also obtained a significantly higher score than the 3.5 version and licensed dentists completing the same exam. Conclusions: The AIbased chatbot tested not only passed the exam but performed better than licensed dentists.

Purpose: To measure the influence of postpolymerization condition (dry and water-submerged) and time (2, 10, 20, and 40 minutes) on the accuracy of additively manufactured model material. Materials and Methods: A bar standard tessellation language (STL) file was used to manufacture all the resin specimens using a 3D printer. Two groups (n = 80 each) were created based on postpolymerization condition: dry (D group) and water-submerged (W group). Each group was then divided into four subgroups (D1 to D4 and W1 to W4; n = 20 each), which were each assigned a postpolymerizing time (2, 10, 20, and 40 minutes). The specimens’ dimensions were measured using a low-force digital caliper. The volume was calculated as follows: V = l × w × h. Shapiro-Wilk test revealed that the data were not normally distributed. Data were analyzed using Kruskal-Wallis and pairwise Mann-Whitney U tests (α = .05). Results: Significant differences in length, width, height, and volume were found among the subgroups (P < .0018). In all groups, the width dimension (x-axis) presented less accuracy compared to height (z-axis) and length (y-axis) (P < .0018). The D2 and D4 subgroups obtained the closest dimensions to the virtual design, and there were no significant differences between these subgroups (P < .0018). The dry condition showed higher manufacturing accuracy than the water-submerged condition. In the water-submerged subgroups, the highest accuracy was obtained in the W2 and W4 subgroups (P < .0018). Conclusions: Postpolymerization condition and time influenced the accuracy of the material tested. The dry postpolymerization condition with times of 10 and 40 minutes obtained the highest accuracy.

Purpose: To systematically review all clinical studies focusing on the clinical outcomes of zirconia restorations bonded to different types of tooth or implant abutments and to provide practical clinical guidelines. Materials and methods: A search was performed for English-language articles in dental journals published up to September 2020 using a combination of free-text words and MeSH terms. Studies were identified for review according to certain inclusion and exclusion criteria. Results: A total of 2,856 studies were identified, and 24 studies were finally included. The included studies featured different types of surface pretreatment methods, primers, resin cements, tooth and implant abutments, zirconia restorations, and designs. The relatively limited number of identified studies and the heterogeneity of the extracted data did not allow for meta-analysis. Conclusion: Airborne-particle abrasion (APA) and tribochemical silica (TBS) APA surface pretreatment methods yielded positive clinical outcomes on zirconia/resin bonding to all types of tooth surfaces identified. Chemically activating the APA and the TBS APA zirconia surfaces with an MDP monomer-based primer, along with an MDP monomer or phosphoric ester monomer-based resin cement, seems to be a durable bonding protocol for all types of tooth surfaces identified. Skipping the surface pretreatment method step in the bonding protocol did not affect the clinical outcomes of certain types of zirconia restorations on most types of tooth or implant abutment surfaces identified. Type of resin cement seems to be a less influential factor.

Purpose: To evaluate the mechanical stability of highly translucent zirconia (Zr) cantilevered fixed dental prostheses (cFDPs) and to investigate the influence of the number of implants (one versus two) supporting cFDPs with different restorative materials on their mechanical stability and load-bearing capacity. Materials and methods: Thirty-two specimens consisting of implant-supported prostheses embedded in resin blocks were fabricated. Sixteen specimens received one implant (bone-level implant, 4.1-mm diameter, 13-mm length; Straumann) to support two-unit cement-retained cFDPs with one extension unit and the other 16 received two implants (bone-level implant, 4.1-mm diameter, 13-mm length; Straumann) positioned corresponding to the missing maxillary central incisors to support three-unit cement-retained cFDPs with one extension unit. Two different prosthetic materials, chromium-cobalt (Cr-Co; Wirobond C+, Bego) and highly translucent Zr (Lava Plus, 3M ESPE) were selected to fabricate the two- and three-unit cFDPs. Standardized twoand three-unit Cr-Co frameworks (CC-I, n = 8; CC-II, n = 8) and highly translucent Zr frameworks (Zr-I, n = 8; Zr-II, n = 8) with a 6-mm cantilever extension were fabricated using CAD/CAM (EOS M 290). Following thermomechanical fatigue loading, the specimens were tested for fracture resistance under static loading. The influence of restoration material and number of supporting implants on fracture resistance were tested using two-way analysis of variance (ANOVA). The level of statistical significance was set below 5% (α < .05). Results: All specimens survived aging. The mean (± standard deviation) fracture resistance values were 416.25 (± 42.71) N for Zr-I, 548.75 (± 75.41) N for Zr-II, 601.0 (± 41.51) N for CC-I, and 664.5 (± 37.59) N for CC-II. CC and Zr group specimens showed significantly different fracture resistance results (P < .001). The number of implants significantly influenced the fracture resistance of Zr groups (P = .001), whereas the influence was not significant for CC groups (P = .089). Conclusion: Within the limitations of this in vitro study, highly translucent zirconia cFDP frameworks demonstrated the potential to withstand reported physiologic occlusal forces applied in the anterior region. The increase in the number of implants supporting zirconia cFDPs significantly contributed to achieving higher fracture resistance values. Keywords: dental implants, fixed dental prosthesis, cantilever, zirconia, highly translucent zirconia

The digital workflow in implant dentistry aims to provide safer and predictable implant placement. This is facilitated through visualization of the anatomical structures as well as the integration of the prosthetic information to dictate implant placement. Guided surgery is useful in edentulous arches planned for fixed rehabilitations, where the unfavorable position of the implants may limit the possibility to realize such a treatment option. In full-arch immediate implant placement and immediate loading, computer-guided implant surgery gains a significant importance. Here, clinicians rely on either soft tissue or bone to provide support for the surgical guide during implant placement. Due to tooth extraction and the loss of anatomical structures that improve the support of the surgical guide, the correct positioning of the guide can be challenging. Further, the procedure that follows to deliver the immediate temporary rehabilitation can also be challenging due to the lack of workflow integrity between the surgical and restorative phases. The present case report describes a novel digital workflow for immediate implant placement and immediate loading of a full-arch rehabilitation, which aims to improve the accuracy of implant placement surgery and simplify the procedure of delivering immediate provisional restorations. (Int J Comput Dent 2022;25(3):303–323; doi: 10.3290/j.ijcd.b3380919) Keywords: immediate loading, guided implant placement, virtual planning, surgical guide, prosthetic guide, digital implantology

Purpose: To investigate the volumetric facial soft tissue changes associated with wearing complete dentures using 3D facial scans. Materials and Methods: A total of 40 volunteers (20 men and 20 women) were recruited for this study and were treated with maxillary and mandibular complete dentures. Six facial scans were taken of each subject; three scans wearing a complete denture and three without the complete denture. The 3D facial scans were taken with the mouth in three positions: closed, relaxed, and smiling. Each scan was superimposed in order to analyze and quantify linear measurements involving 14 soft tissue landmarks and the total volume that the subject gained with the prosthesis. Results: Three variables were evaluated in each analysis: gender, mouth position, and age. In the analysis of the soft tissue landmarks, there was a significant effect of age, with patients > 75 years showing the greatest changes (P < .05). The landmarks that showed the most changes were those located around the mouth. In the volumetric analysis, the variable with the highest influence was gender, with men gaining more volume than women (P < .05). Conclusion: Complete dentures have a significant effect on volumetric changes in perioral tissues. These changes are more marked in patients > 75 years. Compared to women, men depicted greater volumetric changes with complete dentures. These results provide a new avenue for clinicians and developers using facial scans to design future restorations for edentulous patients.

Purpose: To evaluate the mechanical stability and complication rates of titanium (Ti) and zirconia (Zr) abutments restored with cantilevered fixed dental prostheses (cFDPs) when supported by one or two implants.  Materials and Methods: A total of 32 specimens were fabricated. Half of the specimens received one implant, and the other half received two implants to simulate the clinical situation of two or three missing maxillary incisors, respectively. Each group was divided into two subgroups (n = 8). The Ti-1 and Ti-2 groups received Ti abutments (Anatomic Abutment, Straumann) supporting two- or three-unit metal cFDPs, respectively, while Zr-1 and Zr-2 groups received Zr abutments (IPS e.max Anatomic Abutment, Straumann). Following the cementation of cFDPs using resin cement (Multilink Automix, Ivoclar Vivadent), the specimens were subjected to thermomechanical fatigue load and then subsequently loaded until fracture in a universal testing machine. Following the static loading test, stereomicroscopic analyses (Carl Zeiss) were done to identify the weakest component of the cFDP, abutment, and implant assembly. Mann-Whitney U test was used to evaluate the effect of the number of supporting implants and abutment material on fracture strength values, and the level of statistical significance was set at 5% (α = .05). Results: All specimens survived aging, and no screw loosening or fracture was recorded. The mean fracture strength values were 226 N (± 26.45), 551.12 N (± 82.19), 601 N (± 41.51), and 664.5 N (± 37.59) for Zr-1, Zr-2, Ti-1, and Ti-2, respectively. The difference between fracture strength values of the Ti and Zr groups was significant in favor of Ti abutments (P < .001). The number of supporting implants showed a significantly positive effect on the fracture strength of Zr abutments. Conclusion: Zr abutments demonstrated lower fracture strength values than Ti abutments independent from the number of supporting implants when used under cFDPs. Two implant–supported cFDPs with zirconia abutments have the potential to withstand physiologic forces applied in the anterior region.

Purpose: To systematically review all in vitro studies focusing on the durability of different bonding protocols applied to zirconia restorations with different types of tooth or implant abutments. Materials and Methods: English-language articles published in dental journals up to March 2020 were searched using a combination of free text words and MeSH terms, as follows: zirconia; Y-TZP; zirconium; zirconia restoration; enamel; dentin; titanium abutment; titanium base; ceramic abutment; dental core; ceramic or metal post and core; composite build-up; bonding; adhesive cement; resin cement; adhesion; cementation; test; material testing; laboratory; and in vitro. Studies were identified for review based on certain inclusion and exclusion criteria. Results: A total of 3,339 titles were identified. After a successive filtering process, a final total of 28 studies were included. The included studies featured different types of surface pretreatment methods, primers, resin cements, tooth and implant abutments, laboratory test outcomes, and designs. The relatively limited number of the identified studies and the heterogeneity of the extracted data did not allow for meta-analysis. Conclusions: Zirconia surface pretreatment is an essential step in the bonding protocol. Airborne-particle abrasion surface pretreatment had a positive influence on zirconia resin bonding to most types of tooth and implant abutments identified. Chemically activating the treated zirconia surfaces with an MDP monomer-based primer, along with an MDP monomer or phosphate ester monomer-based resin cement, seems to be a durable bonding protocol. Type of resin cement seems to be a less influential factor.

Das Erfassen von Informationen ist für jedwede prothetische Therapie die Grundlage. Es bedarf diverser Daten – angefangen vom gesundheitlichen Allgemeinzustand über Zahn- sowie Parodontalstatus, Knochenqualität sowie -quantität bis hin zur Kiefergelenkposition und zu funktionellen Gegebenheiten. Der Artikel beschreibt die Möglichkeiten, basierend auf der natürlichen Kopfhaltung („Natural head position“, NHP) des Patienten und unter Beachtung der natürlichen Asymmetrien, das Oberkiefermodell positionsecht in den Artikulator zu übertragen. Dies ist die Grundlage für alle weiteren Arbeitsschritte auf dem Weg zum individuellen Zahnersatz. Durch das Zusammenspiel von digitalen Technologien mit analogen Vorgehensweisen reduziert sich die Anzahl der Einproben im Patientenmund. Ergebnis ist ein effizientes Vorgehen, welches zugleich eine hohe Genauigkeit und Reproduzierbarkeit bietet. Keywords: Okklusionsebene, Bissregistrierung, Implantatprothetik, Gesichtsscanner, natürliche Kopfhaltung