Computer-aided design of tooth preparations for automated development of fixed prosthodontics

Morphological effects of input data quantity in AI-powered dental crown design

OBJECTIVES

This retrospective in vitro study evaluated the impact of input data quantity on the morphology of dental crowns generated by AI-based software. The hypothesis suggests that increased input data quantity improves the quality of generated occlusal surfaces.

METHODS

A dataset comprising n=30 patients (11 males, 19 females; age: 22-31 years) was analyzed. Input data was categorized into full dentition (full), quadrant data (quad), and adjacent teeth (adj). AI-based software (Dentbird Crown, Imageworks Inc.) generated crowns for a single lower first molar (36/46). Metrics were proposed to assess the morphology and occlusal relationships of the crowns, with the original tooth as reference.

STATISTICS

Friedman Chi-Square tests, Wilcoxon signed rank tests, Kendall correlation and Fligner-Killeen tests (α = 0.05).

RESULTS

Full and quad groups provided consistent reconstruction quality with no significant differences in morphology and occlusal relationships. The adj group showed significant (p<0.05) morphological deviations and higher reconstruction failure rates compared to the full and quad groups. Correlations (median: (0.19); min-max range: (0.01-0.54) indicate that the proposed metrics capture distinct morphological and functional crown aspects.

CONCLUSION

The software reliably reconstructed crowns with at least quadrant-level input data. Performance declined with reduced input. Full-jaw scans did not enhance accuracy compared to quadrant data.

CLINICAL SIGNIFICANCE

Increased input data quantity can improve the accuracy of AI-based restorations. As a result, prosthodontists benefit from predictable, accurate restoration proposals that reduce the need for digital chairside adjustments as well as manual modifications after fabrication. This streamlines clinical workflows and enhances the quality of restorations. Quadrant-level data has proven sufficient to generate high-quality reconstructions. Further input data did not significantly improve the accuracy of the reconstructions. The proposed metrics enable quantitative assessments of morphological and functional restoration quality, supporting reliable AI-driven workflows.

Generative deep learning approaches for the design of dental restorations: A narrative review

OBJECTIVES

This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.

DATA/SOURCES

PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.

STUDY SELECTION

The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.

CONCLUSIONS

Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.

CLINICAL SIGNIFICANCE

Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.

Three-dimensional Quantification of Enamel Preservation in Tooth Preparation for Porcelain Laminate Veneers: A Fully Digital Workflow In Vitro Study

OBJECTIVE

This in vitro study aimed to evaluate the preservation of enamel after tooth preparation for porcelain laminate veneers (PLVs) at different preparation depths based on a fully digital workflow.

METHODS AND MATERIALS

Sixty extracted human maxillary anterior teeth, including 20 maxillary central incisors (MCIs), 20 maxillary lateral incisors (MLIs), and 20 maxillary canines (MCs) underwent microcomputed tomography (CT) scanning, and were reconstructed as three-dimensional (3D) enamel and dentin models. Subsequently, the three-dimensional (3D) enamel models were imported into Materialise, where each enamel model underwent seven types of virtual preparation for PLVs at preparation depths at 0.1-mm increments from 0.1-0.3-0.5 mm (D1) to 0.7-0.9-1.1 mm (D7). The enamel surface was depicted by merging the virtual preparation and, respective, dentin models. The enamel area and prepared surface were measured to calculate the percentage of enamel (R%). The data were statistically analyzed using one-way analysis of variance (ANOVA) (α=0.05).

RESULTS

The group-wise mean (standard deviation) R values for the MCIs were as follows: D1-D3: 100.00 (0) each, and D4-D7: 74.70 (2.45), 51.40 (5.12), 24.40 (3.06), and 0.00 (0), respectively. The group-wise mean R values for the MLIs were 100.00 (0), 73.70 (3.40), 53.50 (3.44), 25.20 (3.79), and 0.90 (0.99) for the D1-D5 groups, respectively; and 0.00 (0) each for the D6-D7 groups. The group-wise mean (standard deviations) R values for the MCs were as follows: D1-D3: 100.00 (0) each, and D4-D7: 99.00 (1.34), 77.10 (3.28), 74.20 (3.61), and 52.20 (4.09), respectively. The one-way ANOVA revealed significant differences between the seven groups in the MCIs, MLIs, and MCs (p<0.05).

CONCLUSIONS

Our results recommended preparation depths of up to 0.3-0.5-0.7 mm (MCIs), 0.1-0.3-0.5 mm (MLIs), and 0.4-0.6-0.8 mm (MCs) to facilitate complete intraenamel preparation. Moreover, 50% enamel was preserved at preparation depths of 0.5-0.7-0.9 mm (MCIs), 0.3-0.5-0.7 mm (MLIs), and 0.7-0.9-1.1 mm (MCs).

Preliminary application and evaluation of digital step-by-step tooth-preparation templates

STATEMENT OF PROBLEM

Tooth preparation is a fundamental technique, and inaccurate preparation may lead to excessive irreversible tooth removal or insufficient restorative space. The conventional process depends mostly on operator experience, and variable quality is inevitable. Whether a tooth preparation template would be beneficial, especially for inexperienced dentists, is unclear.

PURPOSE

The purpose of this preliminary study was to evaluate the application of new digitally designed step-by-step templates to guide tooth preparation.

MATERIAL AND METHODS

A laboratory scanner was used to obtain digital scans of dental casts. A 3-dimensional reverse engineering software program was used for the step-by-step digital design. The data for a series of guide templates were imported into a computer-aided manufacturing (CAM) machine for milling. Ten experts and 10 inexperienced dentists prepared teeth on a dentoform in a mannequin head. They were instructed to complete the preparation within 20 minutes both with and without the step-by-step template. The prepared crowns were subsequently scanned with an intraoral scanner, the scans were imported into a preparation evaluation software program, and various indexes were scored. The t test was used to analyze the differences between the 2 methods of tooth preparation in each group (α=.05).

RESULTS

No significant differences were found in total scores with and without the guide templates in the expert group (P=.256), but the scores in the inexperienced group differed significantly between the 2 preparation methods (P<.001). In undercut comparisons, the 2 methods of preparation did not differ significantly in the expert (P=.912) or inexperienced groups (P=.601). However, the scores for taper and occlusal reduction were significantly higher in the inexperienced group when using the guide template (P<.001).

CONCLUSIONS

The new digitally designed step-by-step tooth preparation guide template significantly improved the efficiency and quality of tooth preparation for inexperienced dentists when preparing multiple teeth.

A stereolithographic template for computer-assisted teeth preparation in dental esthetic ceramic veneer treatment

OBJECTIVE

This article describes a digital dental esthetic ceramic veneer treatment workflow using a stereolithographic template for teeth preparation.

CLINICAL CONSIDERATIONS

We have presented the case of a 33-year-old woman with dental fluorosis who wanted an esthetic ceramic veneer treatment. A digital smile design was created on a virtual patient, and a virtual diagnostic wax-up was made. Based on the suggested ceramic material thickness, virtual teeth preparation was performed on the diagnostic wax-up. A special-teeth preparation template was then created digitally and fabricated using a stereolithographic technique. This template guided the teeth preparation using a special bur with a stopper. The veneers were fabricated by CAD/CAM and delivered good esthetics and function.

CONCLUSIONS

The stereolithographic tooth reduction template helps realize digital restorative planning. It provides better control of the reduction depth of the labial and incisal preparation, making the operation simpler.

CLINICAL SIGNIFICANCE

The digital dental esthetic ceramic veneer treatment workflow described here using a stereolithographic template for teeth preparation helped with the accurate control of reduction depth for minimally invasive teeth preparation, making the operation simpler, which is a significant improvement over the previous methods.

In vitro precision evaluation of blue light scanning of abutment teeth made with impressions and dental stone casts according to different 3D superimposition methods

PURPOSE

The purpose of this in vitro study was to determine the precision evaluation of blue light scanning of abutment teeth impressions and dental stone casts according to different 3D superimposition methods.

METHODS

Impressions and dental stone casts of the maxillary canine, 1st premolar, and 1st molar were fixed; they were repeatedly scanned 11 times, (6 types, total n = 66). Stereolithography (STL) files were superimposed one by one, and used to obtain 10 root mean square (RMS) values with the 2 superimposition methods (best-fit-alignment, no control). Statistical analysis included the independent t test and one-way ANOVA with Tukey honest significant differences (α = 0.05).

RESULTS

RMS ± Standard Deviation (SD) values for the best-fit-alignment method of the abutment teeth impressions of the maxillary canine, 1st premolar, and 1st molar was 8.07 ± 0.76, 5.03 ± 0.23, and 6.59 ± 0.24, respectively, and those of the no control method were 9.36 ± 0.82, 7.10 ± 1.14, and 8.17 ± 0.36 respectively. RMS ± SD values for the best-fit-alignment method for the dental stone casts were 4.07 ± 0.27, 3.39 ± 0.07, and 3.29 ± 0.07, respectively, and those for the no control method were 6.26 ± 2.50, 4.98 ± 1.16, and 4.55± 0.74, respectively.

CONCLUSIONS

Using different 3D superimposition methods, blue light scanning of abutment teeth impressions and dental stone casts shows high precision. The no control method showed lower precision best-fit-alignment. However, the results may help advance the digital dental CAD/CAM research and the clinical field of Prosthodontics.

Digital evaluation of absolute marginal discrepancy: A comparison of ceramic crowns fabricated with conventional and digital techniques

STATEMENT OF PROBLEM

Marginal discrepancy is key to evaluating the accuracy of fixed dental prostheses. An improved method of evaluating marginal discrepancy is needed.

PURPOSE

The purpose of this in vitro study was to evaluate the absolute marginal discrepancy of ceramic crowns fabricated using conventional and digital methods with a digital method for the quantitative evaluation of absolute marginal discrepancy. The novel method was based on 3-dimensional scanning, iterative closest point registration techniques, and reverse engineering theory.

MATERIAL AND METHODS

Six standard tooth preparations for the right maxillary central incisor, right maxillary second premolar, right maxillary second molar, left mandibular lateral incisor, left mandibular first premolar, and left mandibular first molar were selected. Ten conventional ceramic crowns and 10 CEREC crowns were fabricated for each tooth preparation. A dental cast scanner was used to obtain 3-dimensional data of the preparations and ceramic crowns, and the data were compared with the "virtual seating" iterative closest point technique. Reverse engineering software used edge sharpening and other functional modules to extract the margins of the preparations and crowns. Finally, quantitative evaluation of the absolute marginal discrepancy of the ceramic crowns was obtained from the 2-dimensional cross-sectional straight-line distance between points on the margin of the ceramic crowns and the standard preparations based on the circumferential function module along the long axis.

RESULTS

The absolute marginal discrepancy of the ceramic crowns fabricated using conventional methods was 115 ±15.2 μm, and 110 ±14.3 μm for those fabricated using the digital technique was. ANOVA showed no statistical difference between the 2 methods or among ceramic crowns for different teeth (P>.05).

CONCLUSIONS

The digital quantitative evaluation method for the absolute marginal discrepancy of ceramic crowns was established. The evaluations determined that the absolute marginal discrepancies were within a clinically acceptable range. This method is acceptable for the digital evaluation of the accuracy of complete crowns.

Digital modeling technology for full dental crown tooth preparation

A dental defect is one of the most common oral diseases, and it often requires a full crown restoration. In this clinical operation, the dentist must manually prepare the affected tooth for the full crown so that it has a convergence angle between 4° and 10°, no undercuts, and uniform and even shoulder widths and depths using a high speed diamond bur in the patient׳s mouth within one hour, which is a difficult task that requires visual-manual operation. The quality of the tooth preparation has an important effect on the success rate of the subsequent prosthodontic treatment. This study involved research into digital modeling technology for full dental crown tooth preparation. First, the margin line of the tooth preparation was designed using a semi-automatic interactive process. Second, the inserting direction was automatically computed. Then, the characteristic parameters and the constraints on the tooth preparation were defined for the model. Next, the shoulder and axial surface of the tooth preparation were formed using parametric modeling. Finally, the implicit surface of a radial basis function was used to construct the tooth preparation׳s occlusal surface. The experimental results verified that the method of digital modeling for full crown preparation proposed in this study can quickly and accurately implement personalized designs of various parameters, such as the shoulder width and the convergence angle; it provides a digital design tool for full crown preparation.