Influence of Preparation Design, Marginal Gingiva Location, and Tooth Morphology on the Accuracy of Digital Impressions for Full-Crown Restorations: An In Vitro Investigation

Identification of optimal gingival displacement widths, finish line depths, and preparation designs for digitally scanned single crowns: An in vitro study

STATEMENT OF PROBLEM

Digital scans for single crowns have been reported to achieve accuracy similar to that of traditional impressions in certain patients, but criteria for acquiring high-quality scans are lacking.

PURPOSE

The purpose of this in vitro study was to determine the optimal gingival displacement widths, finish line depths, and preparation designs for single crowns when using intraoral scanners (IOSs) to achieve clinically acceptable and high-quality recordings.

MATERIAL AND METHODS

Three different preparation designs of computer-aided design and computer-aided manufacturing (CAD-CAM) polymethyl methacrylate (PMMA) abutments (round shoulder, shoulder, and sloped shoulder) were fitted with titanium sleeves of 4 different thicknesses (0.2 mm, 0.3 mm, 0.4 mm, and 0.5 mm) and magnetically attracted onto the spindle of an electronic spiral micrometer. Occlusal registration material was injected around the abutment to simulate gingival tissue. After setting, the titanium sleeves were removed to create gaps as different gingival displacement widths. The spiral micrometer was rotated to create 5 different finish line depths (supragingival 0.5 mm, equal gingiva, subgingival 0.5 mm, subgingival 1.0 mm, and subgingival 1.5 mm). Two IOSs (CEREC Primescan [PS] and TRIOS 3 [TS]) were used to scan 3 preparation designs with a combination of 4 gingival displacement widths and 5 finish line depths 5 times each (N=600). The files were imported into an engineering software program and superimposed with the corresponding reference scanned files of the original abutments. Tangent distances (TD) and marginal angle differences (MAD) between the testing groups and references were measured and analyzed using the Kolmogorov-Smirnov, Kruskal-Wallis, and Jonckheere-Terpstra tests (α=.05).

RESULTS

A significant linear trend of decreasing TD and MAD with increasing gingival displacement widths and shallower finish line depths was found. Statistically significant differences (P<.05) were found in TD and MAD between different preparation designs at finish line depths that were equigingival and 0.5-mm subgingival.

CONCLUSIONS

Clinically acceptable scans generally required a gingival displacement width of at least 0.3 mm and a finish line depth within subgingival 1.0 mm. Both IOSs produced high-quality scans for supragingival finish lines. Both IOSs required at least 0.4-mm gingival displacement width to achieve high-quality scans under equal gingival conditions. Only PS with at least 0.4-mm gingival displacement width was able to achieve high-quality scans for subgingival margins. The accuracy ranking of preparation designs was round shoulder (highest), shoulder, and sloped shoulder (lowest).

Effect of preparation design and scan pattern on the accuracy of intraoral scans for complete arch laminate veneers under simulated intraoral variables

PURPOSE

The purpose of the study was to investigate the influence of different preparation designs and scan patterns on the accuracy of intraoral scans for complete-arch maxillary laminate veneers.

MATERIALS AND METHODS

Three maxillary typodonts were used to obtain reference models with three different laminate veneer preparation designs: windows (W), beveled (B), and incisal overlap (IO). Reference scans were obtained with a desktop scanner. A total of 90 complete arch intraoral scans were made with an intraoral scanner (Medit i700) following three different scan patterns: straight motion (SM), zigzag motion (ZM), and combined motion (CM). Ten scans were made in each subgroup and exported as standard tessellation language (STL) files. Assessment of accuracy was conducted with a 3D software analysis program (Geomagic Control X). Each STL file was individually aligned with the reference scan using the best fit algorithm tool, and 3D differences were calculated using the root mean square (RMS) value. Two-way analysis of variance (ANOVA) followed by post hoc comparison tests were applied to analyze precision and trueness data (α = 0.05).

RESULTS

Two-way ANOVA and post hoc comparison tests revealed significant differences among different preparation designs and scan patterns (p < 0.05). Regarding trueness, the IO when scanned with SM presented higher mean RMS than the other preparation designs (W and B) scanned with the same scanning pattern (p < 0.05). Regarding precision, the groups of W and IO presented significantly higher mean RMS than the group of B when scanned with ZM (p < 0.05).

CONCLUSIONS

Accuracy of intraoral scans for complete-arch laminate veneers was affected by different laminate veneer preparation designs and scan patterns.

CLINICAL SIGNIFICANCE

Modifying scan pattern according to preparation design helps to improve scan accuracy for complete-arch laminate veneers.

Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were "artificial intelligence maxillofacial surgery", "machine learning maxillofacial surgery", and "deep learning maxillofacial surgery". Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

Effect of substrate material and abutment geometry on the accuracy of intraoral scanning: An in vitro study

STATEMENT OF PROBLEM

Digital scanning is gradually replacing conventional impression making, but consensus on how tooth preparation influences the accuracy of intraoral scanning is lacking.

PURPOSE

The purpose of this in vitro study was to evaluate the effect of substrate material and abutment geometry on the accuracy of digital casts obtained by intraoral scanning.

MATERIAL AND METHODS

The height and total occlusal convergence (TOC) angle were measured in 5 different groups that contained 5 specimens of different materials: natural tooth, cobalt chromium alloy, titanium, zirconium dioxide ceramic, and resin. The specimens were scanned with an industrial scanner to obtain reference data. Each specimen was placed in a maxillary standard dentition model that was assembled in a head simulator. Each dentition model was scanned 10 times with an intraoral scanner (IOS) under operatory lighting conditions to acquire intraoral scanning files for each specimen. All data were imported into a metrology software program and processed. A total of 10 trueness deviations, the mean superimposition results between IOS scanning data and reference data, and precision deviations, the mean superimposition results between IOS scanning data in pairs, were recorded. Two-way analysis of variance (ANOVA) and Tukey multiple comparison test were used to analyze the accuracy of intraoral scanning in relation to the height or TOC angle of the abutment (α=.05). The total means of each substrate material were compared with the Kruskal-Wallis test and Dunn test for multiple comparisons.

RESULTS

The accuracy of scanning images was related to material and abutment geometry (P<.05). Bias was larger as abutment height increased with most substrates. Larger TOC angles increased the accuracy of the digital scans. The trueness deviation of translucent materials and the precision deviation of reflective materials were generally larger.

CONCLUSIONS

Substrate material and abutment geometry influence the accuracy of intraoral scanning. The accuracy of IOS generally tended to improve with decreasing height and increasing TOC angle and was affected by different substrates.

Trueness and precision of an intraoral scanner on abutments with subgingival vertical margins: An in vitro study

OBJECTIVES

This study aimed to evaluate the accuracy of an intraoral scanner (IOS - Medit i700) on tooth abutments with vertical preparations at 2 depths below the free gingival margin, and to determine if the IOS can reproduce the area beyond the finish surface of the tested preparation geometry.

METHODS

Two abutments for a maxillary first molar were designed by means of CAD software, with vertical preparations set at 1 and 2 mm below the gingiva. These abutments were subsequently printed in resin and placed on a reference model. The reference files consisted of scans made using a metrological machine on these abutments. Ten scans were made with the tested IOS on each sample, resulting in two study groups. The scans from the experimental groups were labeled "V-1″ for vertical preparation at 1 mm below the gingival margin and "V-2″ for 2 mm below. The analysis of these scans was performed using Geomagic Control X (3D SYSTEMS) to assess their trueness and precision in µm. Descriptive statistics with a 95 % confidence interval were employed, alongside independent sample tests, to ascertain any differences between the groups (α=0.05).

RESULTS

Statistically significant differences were not found both for trueness (p=.104) and precision (p=.409), between the tested geometries. The mean values for trueness were V-1 = 37.5[31.4-43.6]; V-2 = 32.6[30.6-34.6]. About the precision, the mean values were V-1 = 20.5[8.4-32.5]; V-2 = 18.4[8.2-28.5]. In both the study groups, it was possible to detect the surface beyond the finish area.

CONCLUSIONS

Within the limitations of this study, vertical preparation design allows for registration of the tooth anatomy beyond the finish area with IOS. Moreover, the mean accuracy values were clinically acceptable at both 1 and 2 mm below the gingival margin.

Influence of Implant Adjacent Teeth on the Accuracy of Digital Impression

OBJECTIVE

 The aim of this study was to evaluate the effect of adjacent teeth patterns on the accuracy of digital scans of parallel and divergent implants for three-unit prostheses.

MATERIALS AND METHODS

 A maxillary typodont model with implants in the locations of the first premolars and first molars was used to develop three clinical scenarios for three-unit prostheses: (S1) Partially edentulous arch with missing first premolars and first molars only; (S2) partially edentulous arch with missing first premolars, second premolars and first molars; and (S3) partially edentulous arch with missing canines, first premolars, second premolars, first molars, and second molars. On one side, the implants were parallel, and for the other side, the implants had a 15-degree buccolingual angle. With the aid of scan bodies, 10 digital impressions were taken for each scenario and for each side. To evaluate the accuracy, a reverse engineering software was used to measure trueness, precision, and interimplant distance.

RESULTS

 The best trueness for parallel implants was observed for S2 (30.0 µm), followed by S3 (67.3 µm) and S1 (74.8 µm) (p < 0.001). Likewise, S2 had the best precision for parallel implants (31.3 µm) followed by S3 (38.0 µm) and S1 (70.3 µm) (p < 0.001). For the divergent implants, S2 exhibited the best trueness (23.1 µm), followed by S3 (48.2 µm) and S1 (59.4 µm) (p = 0.007). Similarly, the S2 had the best precision (12.3 µm) followed by S3 (62.1 µm) and S1 (66.9 µm) (p < 0.001). The S2 had the least interimplant distance deviation followed by S1 and S3. The difference was significant for parallel implants (p = 0.03), but insignificant for divergent implants (p = 0.15).

CONCLUSION

 Regardless of the presenting scenario, digital implant impressions for three-unit prostheses appear to be clinically accurate. A clear interimplant area between scan bodies enhanced the accuracy of digital impressions. This observation can be attributed to more accessible axial surface scanning of the scan body.

Comparing the accuracy of distinct scanning systems and their impact on marginal/internal adaptation of tooth-supported indirect restorations. A scoping review

OBJECTIVE

To summarize the existing scientific evidence on the effect of distinct intraoral (IOS) and extraoral (EOS) scanners in terms of their accuracy for image acquisition and the marginal/internal adaptation of indirect restorations.

METHODS

The protocol of this scoping review is available online (https://osf.io/cwua7/). A structured search, with no date restriction, was performed in LILACS, MEDLINE via Pubmed, EMBASE, Web of Science, and Scopus, for articles written in English. The inclusion criteria were studies that considered at least two scanners, regardless of method (intra or extraoral), for the production of tooth-supported restorations. Two independent and blinded researchers screened the studies, collected and analyzed the data descriptively.

RESULTS

103 studies were included (55 on marginal/internal adaptation, 33 on accuracy, 5 on both outcomes, and 10 reviews). Most of them, shown clinically acceptable adaptation (<120 μm). Factors commonly related to the performance of scanners are: use of anti-reflection powders, method of image acquisition, and restoration/tooth characteristics. The need of anti-reflection powders was controversial. Different scanning principles seems to result on similar performance; IOS that combine them could be promising. The most explored systems were Omnicam - IOS, and inEos X5 - EOS, which showed similar performance on marginal/internal adaptation. Scarce studies explored the performance of EOS systems, especially in terms of accuracy. Different restoration designs as single-unit seemed not to modify the performance of scanners. Limited information is available regarding the planned cement space, restorative material and design (multi-unit restorations), as also techniques to measure adaptation.

CONCLUSIONS

Digital scanners are valid approaches to obtain accurate impressions resulting in clinically acceptable restorations. Systems that uses combined principles of image acquisition seems promising for optimal performance. Based on high discrepancy, the quality of evaluated evidence is low, and well-designed studies are still encouraged, especially considering validated IOS/EOS as a control comparison condition.

Effect of preparation type on the accuracy of different intraoral scanners: An in vitro study at different levels of accuracy evaluation

OBJECTIVE

Evaluation of the effect of preparation type (inlay, onlay, and crown) on the accuracy of different intraoral scanning (IOS) systems at the preparation and arch segment levels.

MATERIALS AND METHODS

Three molars were prepared for inlay, onlay, and crown. Each preparation was scanned 10 times by CEREC Omnicam, Trios 3 (TS), and Medit i500 scanners. Each image was trimmed twice. The first trimming produced a preparation image (PI), and the second trimming extracted a segment image (SI) that involved the preparation with the adjacent teeth. Trueness and precision were calculated at the PI and SI levels.

RESULTS

At the PI level, all IOS systems had similar trueness pattern for all preparations, where the inlay had the best trueness followed by the crown and onlay. At the SI level, the different preparations showed similar trueness. The precision did not show a clear pattern of superiority for any preparation. The TS was significantly more precise than other IOS systems at the PI and SI levels, for every preparation. The proximal areas suffered from the greatest errors, regardless of preparation type.

CONCLUSIONS

The preparation type influenced PI trueness, and the IOS system affected PI and SI precisions.

CLINICAL SIGNIFICANCE

The smaller and less complex preparations have greater IOS accuracy than larger and more complex preparations. As the proximal areas are more affected regardless of the preparation, a more accessible proximal area for scanning is desirable.

Consensus on the preparation margin and restoration margin in ceramic esthetic rehabilitation

In esthetic rehabilitation, methods used to enhance the margin quality have always been the focus and difficulty of improving the level of diagnosis and treatment, prevention and treatment of complications, and collaboration between clinicians and technicians. However, it is impeded by the ambiguous definition and classification of margin, unstandardized tooth preparation, manufacturing process of restoration, and lack of reliable means of checking the quantitative requirements of preparation or restoration. The digital technologies that are increasingly applied, such as intra-oral scanner, impression scanner, and computerized numerical control cutting machine, have strict requirements about margin quality. Failure of recognizing margins by these scanners will hinder the digital process of diagnosis and treatment. Even if these sharp and narrow margins are successfully scanned, they cannot be milled accurately. To overcome these problems, this article demonstrated the clear and complete definition of preparation margin and restoration margin, as well as their subclassifications, by analyzing the target restoration space from a geometric perspective. Practical approaches to measuring the margin width and inspecting the margin quality were proposed. The new and full understanding and proposal about preparation margin and restoration margin characterized by measurements will effectively support the thoroughly digitalized process of esthetic rehabilitation using porcelain in fixed prosthodontics, which is based on the guidance of values.

A novel reference model for dental scanning system evaluation: analysis of five intraoral scanners

PURPOSE

MATERIALS AND METHODS

RESULTS

CONCLUSION

Intraoral Digital Scans for Fabricating Tooth-Supported Prostheses Using a Custom Intraoral Scan Body

This article describes a technique to assist with intraoral digital scans for fabricating tooth-supported prostheses by using a custom intraoral scan body when the extension of the scan or the clinical characteristics might compromise the reliability of the intraoral digital scan. A preliminary intraoral scan of the tooth preparations is used to design a custom intraoral scan body which is manufactured using polymethylmethacrylate and a 5-axis milling machine. A low-viscosity polyvinyl siloxane impression of the tooth preparations is obtained using the custom intraoral scan body. Subsequently, the custom intraoral scan body is digitized using an intraoral scanner. A design software program is used to align the digitized custom intraoral scan body with the preliminary intraoral scan to obtain the definitive virtual cast. This technique aims to reduce manual conventional laboratory procedures such as pouring dental impression or die trimming which might minimize inaccuracies on the virtual definitive cast.

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

Computer Aided Design Modelling and Finite Element Analysis of Premolar Proximal Cavities Restored with Resin Composites

This study evaluated the stress distribution in five different class II cavities of premolar models restored with conventional or bulk-fill flowable composite by means of finite element analysis (FEA) under shrinkage and occlusal loading. An upper validated premolar model was imported in the software, and five class II cavities with different occlusal extensions and dimensions were prepared: horizontal cavity on the mesial surface (horizontal slot), mesio-occlusal cavity, mesial cavity (vertical slot), tunnel type cavity and direct access cavity. The models were restored with conventional or bulk-fill flowable resin composite. The tested materials were considered as homogeneous, linear, and isotropic. The Maximum Principal Stress criteria was chosen to evaluate the tensile stress results. The lowest shrinkage stress value was observed in the direct access cavity restored with bulk-fill flowable resin composite (36.12 MPa). The same cavity, restored with conventional composite showed a score of 36.14 MPa. The horizontal slot cavity with bulk-fill flowable showed a score of 46.71 MPa. The mesio-occlusal cavity with bulk-fill flowable had a score of 53.10 MPa, while with conventional composite this was 55.35 MPa. Higher shrinkage stress was found in the vertical slot cavity with conventional resin 56.14 MPa, followed by the same cavity with bulk-fill flowable 56.08 MPa. Results indicated that the use of bulk-fill flowable composite resin more significantly decreased the polymerization shrinkage stress magnitude. The larger the cavity and the volume of material necessary to restore the tooth, the greater the residual stress on enamel and dentin tissue.