Newly introduced provisional crowns and fixed dental prostheses (FDP) materials should exhibit good physical and mechanical properties necessary to serve the purpose of their fabrication. The aim of this systematic literature review and meta-analysis is to evaluate the articles comparing the physical and mechanical properties of 3D-printed provisional crown and FDP resin materials with CAD/CAM (Computer-Aided Designing/Computer-Aided Manufacturing) milled and conventional provisional resins. Indexed English literature up to April 2022 was systematically searched for articles using the following electronic databases: MEDLINE-PubMed, Web of Science (core collection), Scopus, and the Cochrane library. This systematic review was structured based on the guidelines given by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The focused PICO/PECO (Participant, Intervention/exposure, Comparison, Outcome) question was: ‘Do 3D-printed (P) provisional crowns and FDPs (I) have similar physical and mechanical properties (O) when compared to CAD/CAM milled and other conventionally fabricated ones (C)’. Out of eight hundred and ninety-six titles, which were recognized after a primary search, twenty-five articles were included in the qualitative analysis, and their quality analysis was performed using the modified CONSORT scale. Due to the heterogeneity of the studies, only twelve articles were included for quantitative analysis. Within the limitations of this study, it can be concluded that 3D-printed provisional crown and FDP resin materials have superior mechanical properties but inferior physical properties compared to CAD/CAM milled and other conventionally fabricated ones. Three-dimensionally printed provisional crowns and FDP materials can be used as an alternative to conventional and CAD/CAM milled long-term provisional materials.

Background and Objectives: The present systematic review and meta-analysis undertake a comparison of studies that examine the accuracy of robot-assisted dental implant placement in relation to static computer-assisted implant surgery (SCAIS), dynamic computer-assisted implant surgery (DCAIS), and freehand procedures. This study aims to provide a comprehensive understanding of the precision of robot-assisted dental implant placement and its comparative efficacy in relation to other placement techniques. Methods: The guidelines recommended by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were used to organize and compose this review. Four electronic databases (PubMed, Web of Science, Scopus, and Cochrane) were systematically searched for pertinent articles. Articles were selected following the inclusion and exclusion criteria. Qualitative and quantitative analyses of the selected articles were performed. Results: The initial electronic search resulted in 1087 hits. Based on the inclusion and exclusion criteria, five articles were selected for qualitative analysis, out of which three were considered for quantitative analysis. Three parameters were considered for accuracy evaluation (angular, coronal, and apical deviation). The mean angular deviation was −1.22 degrees (95% CI, −1.06–−1.39), the mean coronal deviation was −0.15 mm (95% CI, −0.24–−0.07), and the mean apical deviation was −0.19 mm (95% CI, −0.27–−0.10). Conclusions: The robotic implant system was found to have significantly lower angular deviations and insignificantly lower coronal and apical deviations compared to DCAIS. Within the limitations of this review, it can be concluded that robot-assisted implant placement in resin models permits higher accuracy compared to DCAIS and SCAIS systems. However, due to the limited number of comparative studies with high heterogeneity, the findings of this review should be interpreted with caution. Further research is necessary to confirm the clinical application of robotics in implant surgery.

Crown lengthening surgery and deep margin elevation are two distinct approaches used to manage decayed teeth. This systematic review examined the survival rate of badly decayed teeth when restored using the crown lengthening technique and compared it to the deep margin elevation technique. The search was conducted during July 2020 and then again updated at the end of July 2021, and no restriction concerning publication status and time was applied during the search. Cochrane Database, EBSCO, Scopus, and Medline databases were searched electronically for relevant literature. Google Scholar was used as a secondary source. Predefined inclusion and exclusion criteria were used to select the relevant articles. PRISMA guidelines were followed. The focused PICO question was: ‘Does the crown lengthening technique (I) provide a better survival rate (O) than deep margin elevation technique (C) following the restoration of badly decayed teeth (P).’ A total of six articles were included after performing screening based on the eligibility criteria. Four studies focused on crown lengthening while two focused on deep margin elevation technique. A majority of the studies showed a high risk of bias owing to methodological insufficiencies. Crown lengthening (CL) treated cases showed a change in the free gingival margin at six months post-surgery. A tissue rebound was seen that was correlated to the periodontal biotype. Teeth treated with the deep margin elevation (DME) technique showed high survivability. There is a lack of high-quality trials examining surgical comparisons between CL and DME with long-term follow-up. Patient- and dentist-reported outcomes have not been given adequate consideration in the literature. Based on the limited evidence, it can be concluded that for restorative purposes, crown lengthening surgery can be successful in long-term retention of restored teeth. However, the deep margin elevation technique has a better survival ratio. Future well-designed and executed research will have an effect on the evidence and level of certainty for the best approach to treating severely decayed teeth.

The aim of this systematic review was to evaluate the marginal fit and internal adaptation of provisional crowns and fixed dental prostheses (FDPs) fabricated using 3D-printing resins and compared them with those fabricated by CAD/CAM (computer-aided designing/computer-aided manufacturing) milling and conventional resins. The null hypotheses tested were that there would be no differences in the marginal fit and internal adaptation of 3D-printed provisional crowns and FDP resins when compared to CAD/CAM-milled and conventional provisional resins. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to construct this systematic review. The focused PICO/PECO (Population, Intervention/Exposure, Comparison, Outcome) question was “Do provisional crowns and FDPs (P) fabricated by 3D-printing (I) have similar marginal adaptation and internal fit (O) when compared to those fabricated by CAD/CAM milling and conventional techniques (C)?”. The protocol used for this systematic review was pre-registered in the International Prospective Register of Systematic Reviews (PROSPERO). Electronic databases (e.g., MEDLINE/PubMed and Web of Science (Core Collection)) were systematically searched for indexed English literature published up to June 2022. In the initial electronic search of the selected databases, 519 articles were identified. Duplicates were removed, and screening was performed to select the articles that met the preset inclusion criteria. Sixteen studies were selected for qualitative analysis, but only ten of them provided comparative data and were selected for quantitative analysis. The modified CONSORT scale was used for qualitative analysis, and most of the included studies were rated to be of moderate quality. Based on the findings, it could be concluded that provisional crowns and FDPs fabricated from 3D-printing resins have a superior marginal fit and internal adaptation when compared to CAD/CAM-milled and conventional provisional resins; thus, they can be used as a dependable alternative to other resins.

Objectives: The purpose of the study is to evaluate the diagnostic accuracy of artificial intelligence (AI) software in detecting a common set of periodontal and restorative conditions, including marginal bone loss, dental caries, periapical lesions, calculus, endodontic treatment, crowns, restorations, and open crown margins, using intraoral periapical radiographs. Additionally, the study will assess how this AI software influences the diagnostic accuracy of dentists with varying levels of experience in identifying these conditions. Methods: A total of three hundred digital IOPARs representing 1030 teeth were selected based on predetermined selection criteria. The parameters assessed included (a) calculus, (b) periapical radiolucency, (c) caries, (d) marginal bone loss, (e) type of restorative (filling) material, (f) type of crown retainer material, and (g) detection of open crown margins. Two oral radiologists performed the initial diagnosis of the selected radiographs and independently labeled all the predefined parameters for the provided IOPARs under standardized conditions. This data served as reference data. A pre-trained AI-based computer-aided detection (“CADe”) software (Second Opinion^®, version 1.1) was used for the detection of the predefined features. The reports generated by the AI software were compared with the reference data to evaluate the diagnostic accuracy of the AI software. In the second phase of the study, thirty dental interns and thirty dental specialists were randomly selected. Each participant was randomly assigned five IOPARs and was asked to detect and diagnose the predefined conditions. Subsequently, all the participants were requested to reassess the IOPARs, this time with the assistance of the AI software. All the data was recorded using a self-designed Performa. Results: The sensitivity of the AI software in detecting caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 91.0%, 86.6%, 97.1%, 82.6%, 89.3%, 93.4%, 80.2%, and 91.1%, respectively. The specificity of the AI software in detected caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 87%, 98.3%, 99.6%, 91.9%, 96.4%, 99.3%, 97.8%, and 93.1%, respectively. The differences between the AI software and radiologist diagnoses of caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss were statistically significant (all p values < 0.0001). The results showed that the diagnostic accuracy of operators (interns and specialists) with AI software revealed higher accuracy, sensitivity, and specificity in detecting caries, PA lesions, restoration, endodontic treatment, calculus, and marginal bone loss compared to that without using AI software. There were variations in the improvements in the diagnostic accuracy of interns and dental specialists. Conclusions: Within the limitations of the study, it can be concluded that the tested AI software has high accuracy in detecting the tested dental conditions in IOPARs. The use of AI software enhanced the diagnostic capabilities of dental operators. The present study used AI software to detect a clinically useful set of periodontal and restorative conditions, which can help dental operators in fast and accurate diagnosis and provide high-quality treatment to their patients.

The current literature lacks substantial information about the effect of denture cleansers on the color stability of denture bases formed using Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM) additive and CAD/CAM subtractive manufacturing techniques. This study aimed to assess the effect of two commercially available denture cleansers on the color stability of denture base resins fabricated using four different techniques. Forty-five disc-shaped specimens were fabricated using each technique. Initial color readings were recorded. Specimens were randomly divided into three subgroups (n = 15): a control group (distilled water) and two denture cleanser groups. They were immersed in these solutions, simulating 180 days of use. Final color readings were recorded. The color difference was calculated, and the data were statistically analyzed. For all the specimens, significant color changes were observed after immersion in denture cleanser solutions. The extent of color change varied according to the type of denture cleanser used. When placed in the denture cleanser solutions, the CAD/CAM subtractive group showed the maximum color change (−1.10 and −0.72), while the CAD/CAM additive (3D printing) groups showed the least color change (−0.48 and −0.54). Clinicians should choose appropriate denture cleansers for newly introduced denture base resins to minimize the changes in the color of the dentures.