Morphology and mechanical performance of dental crown designed by 3D-DCGAN

Application of artificial intelligence in dental crown prosthesis: a scoping review

BACKGROUND

In recent years, artificial intelligence (AI) has made remarkable advancements and achieved significant accomplishments across the entire field of dentistry. Notably, efforts to apply AI in prosthodontics are continually progressing. This scoping review aims to present the applications and performance of AI in dental crown prostheses and related topics.

METHODS

We conducted a literature search of PubMed, Scopus, Web of Science, Google Scholar, and IEEE Xplore databases from January 2010 to January 2024. The included articles addressed the application of AI in various aspects of dental crown treatment, including fabrication, assessment, and prognosis.

RESULTS

The initial electronic literature search yielded 393 records, which were reduced to 315 after eliminating duplicate references. The application of inclusion criteria led to analysis of 12 eligible publications in the qualitative review. The AI-based applications included in this review were related to detection of dental crown finish line, evaluation of AI-based color matching, evaluation of crown preparation, evaluation of dental crown designed by AI, identification of a dental crown in an intraoral photo, and prediction of debonding probability.

CONCLUSIONS

AI has the potential to increase efficiency in processes such as fabricating and evaluating dental crowns, with a high level of accuracy reported in most of the analyzed studies. However, a significant number of studies focused on designing crowns using AI-based software, and these studies had a small number of patients and did not always present their algorithms. Standardized protocols for reporting and evaluating AI studies are needed to increase the evidence and effectiveness.

Deep learning-designed implant-supported posterior crowns: Assessing time efficiency, tooth morphology, emergence profile, occlusion, and proximal contacts

OBJECTIVES

To compare implant supported crowns (ISCs) designed using deep learning (DL) software with those designed by a technician using conventional computer-aided design software.

METHODS

Twenty resin-based partially edentulous casts (maxillary and mandibular) used for fabricating ISCs were evaluated retrospectively. ISCs were designed using a DL-based method with no modification of the as-generated outcome (DB), a DL-based method with further optimization by a dental technician (DM), and a conventional computer-aided design method by a technician (NC). Time efficiency, crown contour, occlusal table area, cusp angle, cusp height, emergence profile angle, occlusal contacts, and proximal contacts were compared among groups. Depending on the distribution of measured data, various statistical methods were used for comparative analyses with a significance level of 0.05.

RESULTS

ISCs in the DB group showed a significantly higher efficiency than those in the DM and NC groups (P ≤ 0.001). ISCs in the DM group exhibited significantly smaller volume deviations than those in the DB group when superimposed on ISCs in the NC group (DB-NC vs. DM-NC pairs, P ≤ 0.008). Except for the number and intensity of occlusal contacts (P ≤ 0.004), ISCs in the DB and DM groups had occlusal table areas, cusp angles, cusp heights, proximal contact intensities, and emergence profile angles similar to those in the NC group (P ≥ 0.157).

CONCLUSIONS

A DL-based method can be beneficial for designing posterior ISCs in terms of time efficiency, occlusal table area, cusp angle, cusp height, proximal contact, and emergence profile, similar to the conventional human-based method.

CLINICAL SIGNIFICANCE

A deep learning-based design method can achieve clinically acceptable functional properties of posterior ISCs. However, further optimization by a technician could improve specific outcomes, such as the crown contour or emergence profile angle.

Isotopological remeshing and statistical shape analysis: Enhancing premolar tooth wear classification and simulation with machine learning

OBJECTIVE

The aim of this study was to evaluate the accuracy of a combined approach based on an isotopological remeshing and statistical shape analysis (SSA) to capture key anatomical features of altered and intact premolars. Additionally, the study compares the capabilities of four Machine Learning (ML) algorithms in identifying or simulating tooth alterations.

METHODS

113 premolar surfaces from a multicenter database were analyzed. These surfaces were processed using an isotopological remeshing method, followed by a SSA. Mean Euclidean distances between the initial and remeshed STL files were calculated to assess deviation in anatomical landmark positioning. Seven anatomical features were extracted from each tooth, and their correlations with shape modes and morphological characteristics were explored. Four ML algorithms, validated through three-fold cross-validation, were assessed for their ability to classify tooth types and alterations. Additionally, twenty intact teeth were altered and then reconstructed to verify the method's accuracy.

RESULTS

The first five modes encapsulated 76.1% of the total shape variability, with a mean landmark positioning deviation of 10.4 µm (±6.4). Significant correlations were found between shape modes and specific morphological features. The optimal ML algorithms demonstrated high accuracy (>83%) and precision (>86%). Simulations on intact teeth showed discrepancies in anatomical features below 3%.

CONCLUSION

The combination of an isotopological remeshing with SSA showed good reliability in capturing key anatomical features of the tooth.

CLINICAL SIGNIFICANCE

The encouraging performance of ML algorithms suggests a promising direction for supporting practitioners in diagnosing and planning treatments for patients with altered teeth, ultimately improving preventive care.

Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study

INTRODUCTION AND AIMS

Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software.

METHODS

A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal-Wallis test (α = 0.05).

RESULTS

The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05).

CONCLUSION

AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.

Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review

Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; "Artificial Intelligence", "Machine Learning", "Neural Networks", "Virtual Reality", "Digital Dentistry", "CAD/CAM", and "Restorative Dentistry". Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners' CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field.

Applications and performance of artificial intelligence models in removable prosthodontics: A literature review

PURPOSE

In this narrative review, we present the current applications and performances of artificial intelligence (AI) models in different phases of the removable prosthodontic workflow and related research topics.

STUDY SELECTION

A literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases between January 2010 and January 2023. Search terms related to AI were combined with terms related to removable prosthodontics. Articles reporting the structure and performance of the developed AI model were selected for this literature review.

RESULTS

A total of 15 articles were relevant to the application of AI in removable prosthodontics, including maxillofacial prosthetics. These applications included the design of removable partial dentures, classification of partially edentulous arches, functional evaluation and outcome prediction in complete denture treatment, early prosthetic management of patients with cleft lip and palate, coloration of maxillofacial prostheses, and prediction of the material properties of denture teeth. Various AI models with reliable prediction accuracy have been developed using supervised learning.

CONCLUSIONS

The current applications of AI in removable prosthodontics exhibit significant potential for improving the prosthodontic workflow, with high accuracy levels reported in most of the reviewed studies. However, the focus has been predominantly on the diagnostic phase, with few studies addressing treatment planning and implementation. Because the number of AI-related studies in removable prosthodontics is limited, more models targeting different prosthodontic disciplines are required.

AI's Role in Improving Social Connection and Oral Health for Older Adults: A Synergistic Approach

KNOWLEDGE TRANSFER STATEMENT

This study explored how artificial intelligence (AI) can revolutionize geriatric care by improving oral health and alleviating social disconnection among isolated older adults. The findings can guide clinicians in integrating AI tools into practices, assist policymakers in developing AI-inclusive health policies, and inform patients about the potential benefits of AI in enhancing their health outcomes and social connection.

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.

Precise tooth design using deep learning-based templates

OBJECTIVES

In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration.

METHODS

Ninety models of right maxillary central incisor (80 for training, 10 for validation) were collected. A DL model named ToothDIT was trained to establish an implicit template and a neural network capable of predicting unique identifications. In the validation stage, teeth in validation set were processed into corner, incisive, and medium defects. The defective teeth were inputted into ToothDIT to predict the unique identification, which actuated the deformation of the implicit template to generate the highly customized template (DIT) for the target tooth. Morphological restorations were executed with templates from template shape library (TSL), average tooth template (ATT), and DIT in Exocad (GmbH, Germany). RMSestimate, width, length, aspect ratio, incisal edge curvature, incisive end retraction, and guiding inclination were introduced to assess the restorative accuracy. Statistical analysis was conducted using two-way ANOVA and paired t-test for overall and detailed differences.

RESULTS

DIT displayed significantly smaller RMSestimate than TSL and ATT. In 2D detailed analysis, DIT exhibited significantly less deviations from the natural teeth compared to TSL and ATT.

CONCLUSION

The proposed DL model successfully reconstructed the morphology of anterior teeth with various degrees of defects and achieved satisfactory accuracy. This approach provides a more reliable reference for prostheses design, resulting in enhanced accuracy in morphological restoration.

CLINICAL SIGNIFICANCE

This DL model holds promise in assisting dentists and technicians in obtaining morphology templates that closely resemble the original shape of the defective teeth. These customized templates serve as a foundation for enhancing the efficiency and precision of digital restorative design for defective teeth.

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning

Background and objective

Due to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.

Methods

A Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.

Results

The reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.

Conclusions

This article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.

Effects of design software program and manufacturing method on the marginal and internal adaptation of esthetic crowns for primary teeth: A microcomputed tomography evaluation

STATEMENT OF PROBLEM

The adaptation of digitally produced crowns is affected by the design software program and manufacturing method. The effect of artificial intelligence (AI) software program design on the adaptation of the crowns is unclear and comparative evaluations should be documented.

PURPOSE

The purpose of this study was to assess the marginal and internal gaps, the absolute marginal discrepancies, and the 3-dimensional (3D) discrepancy volumes of the resin-based milled and 3D printed crowns for primary teeth designed with computer-aided design (CAD) and AI software programs by using microcomputed tomography (µCT).

MATERIAL AND METHODS

A total of 40 resin-based esthetic crowns were produced for a prepared typodont tooth (right mandibular primary second molar) according to the design software program (CAD and AI) and manufacturing method (milling and 3D printing) (n=10). Four experimental groups were generated as CAD-milled, CAD-3D printed, AI-milled, and AI-3D printed. The marginal, axial, and occlusal gap values, the absolute marginal discrepancies, and the 3D discrepancy volumes of the specimens were measured by using µCT. The data were analyzed by using 2-way ANOVA and the Tukey HSD tests (α=.05).

RESULTS

The lowest value for the marginal gap (54 ±43 µm) was observed in the CAD-milled group and the marginal gap value of the AI-3D printed group was significantly lower than the AI-milled group (P<.05). The lowest value for the axial gap (63 ±7 µm) was observed in the AI-3D printed group, and the highest value (145 ±58 µm) was observed in the CAD-milled group; the result for the occlusal gap value was opposite. The highest absolute marginal discrepancy value was observed in the CAD-milled group. The 3D discrepancy volumes increased in the order of the CAD-3D printed, AI-milled, CAD-milled, and AI-3D printed groups.

CONCLUSIONS

The marginal and internal gap values of the resin-based crowns were affected by the design software program and manufacturing method; however, tested groups showed clinically acceptable gap values.

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study

OBJECTIVES

This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software.

METHODS

Thirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann-Whitney test, one-way ANOVA, and Kruskal-Wallis test with post hoc pairwise comparisons (α = 0.05).

RESULTS

The AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001).

CONCLUSIONS

Crowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points.

CLINICAL SIGNIFICANCE

DL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

OBJECTIVE

The unique structure of human teeth limits dental repair to custom-made solutions. The production process requires a lot of time and manpower. At present, artificial intelligence (AI) has begun to be used in the medical field and improve efficiency. This study attempted to design a variety of dental restorations using AI and evaluate their clinical applicability.

METHODS

Using inlay and crown restoration types commonly used in dental standard models, we compared differences in artificial wax-up carving (wax-up), artificial digital designs (digital) and AI designs (AI). The AI system was designed using computer calculations, and the other two methods were designed by humans. Restorations were made by 3D printing resin material. Image evaluations were compared with cone beam computed tomography (CBCT) by calculating the root mean squared error.

RESULTS

Surface truth results showed that AI (68.4 µm) and digital-designed crowns (51.0 µm) had better reproducibility. Using AI for the crown reduced the time spent by 400% (compared to digital) and 900% (compared to wax-up). Optical microscopic and CBCT images showed that AI and digital designs had close margin gaps (p < 0.05). The margin gap of the crown showed that the wax-up group was 4.1 and 4.3 times greater than those of the AI and digital crowns, respectively. Therefore, the utilization of artificial intelligence can assist in the production of dental restorations, thereby enhancing both production efficiency and accuracy.

SIGNIFICANCE

It is expected that the development of AI can contribute to the reproducibility, efficiency, and goodness of fit of dental restorations.

Time efficiency, occlusal morphology, and internal fit of anatomic contour crowns designed by dental software powered by generative adversarial network: A comparative study

OBJECTIVES

To evaluate the time efficiency, occlusal morphology, and internal fit of dental crowns designed using generative adversarial network (GAN)-based dental software compared to conventional dental software.

METHODS

Thirty datasets of partial arch scans for prepared posterior teeth were analyzed. Each crown was designed on each abutment using GAN-based software (AI) and conventional dental software (non-AI). The AI and non-AI groups were compared in terms of time efficiency by measuring the elapsed work time. The difference in the occlusal morphology of the crowns before and after design optimization and the internal fit of the crown to the prepared abutment were also evaluated by superimposition for each software. Data were analyzed using independent t tests or Mann-Whitney test with statistical significance (α=.05).

RESULTS

The working time was significantly less for the AI group than the non-AI group at T1, T5, and T6 (P≤.043). The working time with AI was significantly shorter at T1, T3, T5, and T6 for the intraoral scan (P≤.036). Only at T2 (P≤.001) did the cast scan show a significant difference between the two groups. The crowns in the AI group showed less deviation in occlusal morphology and significantly better internal fit to the abutment than those in the non-AI group (both P<.001).

CONCLUSIONS

Crowns designed by AI software showed improved outcomes than that designed by non-AI software, in terms of time efficiency, difference in occlusal morphology, and internal fit.

CLINICAL SIGNIFICANCE

The GAN-based software showed better time efficiency and less deviation in occlusal morphology during the design process than the conventional software, suggesting a higher probability of optimized outcomes of crown design.

Evaluating the performance of generative adversarial network-synthesized periapical images in classifying C-shaped root canals

This study evaluated the performance of generative adversarial network (GAN)-synthesized periapical images for classifying C-shaped root canals, which are challenging to diagnose because of their complex morphology. GANs have emerged as a promising technique for generating realistic images, offering a potential solution for data augmentation in scenarios with limited training datasets. Periapical images were synthesized using the StyleGAN2-ADA framework, and their quality was evaluated based on the average Frechet inception distance (FID) and the visual Turing test. The average FID was found to be 35.353 (± 4.386) for synthesized C-shaped canal images and 25.471 (± 2.779) for non C-shaped canal images. The visual Turing test conducted by two radiologists on 100 randomly selected images revealed that distinguishing between real and synthetic images was difficult. These results indicate that GAN-synthesized images exhibit satisfactory visual quality. The classification performance of the neural network, when augmented with GAN data, showed improvements compared with using real data alone, and could be advantageous in addressing data conditions with class imbalance. GAN-generated images have proven to be an effective data augmentation method, addressing the limitations of limited training data and computational resources in diagnosing dental anomalies.

Measurement of proximal contact of single crowns to assess interproximal relief: A pilot study

Background

It is common for dental technicians to adjust the proximal surface of adjacent teeth on casts when fabricating single crowns. However, whether the accuracy of the proximal contact is affected if this step is eliminated is unclear.

Objective

To evaluate the accuracy of the proximal contact of single crowns for mandibular first molars fabricated from four different restorative materials, without adjustment of the proximal surface of the adjacent teeth by the laboratory/dental technician.

Methods

This study was in vitro; all the clinical procedures were conducted on a dentoform. The mandibular first molar tooth on the dentoform was prepared using diamond burs and a high speed handpiece. Twenty single crowns were fabricated, five for each group (monolithic zirconia, lithium disilicate, metal ceramic, and cast gold). No proximal surface adjacent to the definitive crowns was adjusted for tight contact in the dental laboratory. Both the qualitative analyses, using dental floss and shimstock, and the quantitative analyses, using a stereo microscope, were performed to evaluate the accuracy of the proximal contact of the restoration with the adjacent teeth. In the quantitative analysis, one-way analysis of variance was used to compare mean values at a significance level of 0.05.

Results

In quantitative analysis, the differences between the proximal contact tightness of the four groups was not statistically significant (P = 0.802 for mesial contacts, P = 0.354 for distal contacts). In qualitative analysis, in most crowns, dental floss passed through the contact with tight resistance and only one film of shimstock could be inserted between the adjacent teeth and the restoration. However, one specimen from the cast gold crown had open contact.

Conclusions

Even without proximal surface adjustment of the adjacent teeth during the crown fabrication process, adequate proximal contact tightness between the restoration and adjacent teeth could be achieved.

Revolutionising dental technologies: a qualitative study on dental technicians' perceptions of Artificial intelligence integration

BACKGROUND

The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians' views on the use of AI in dental technology are still sparse in the literature. This qualitative study aimed to explore the perceptions of dental technicians regarding the use of AI in their dental laboratory practice.

METHODS

Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts.

RESULTS

The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support.

CONCLUSION

This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.

Feasibility of software-based assessment for automated evaluation of tooth preparation for dental crown by using a computational geometric algorithm

The purpose of this study was to propose the concept of software-based automated evaluation (SAE) of tooth preparation quality using computational geometric algorithms, and evaluate the feasibility of SAE in the assessment of abutment tooth preparation for single-unit anatomic contour crowns by comparing it with a human-based digitally assisted evaluation (DAE) by trained human evaluators. Thirty-five mandibular first molars were prepared for anatomical contour crown restoration by graduate students. Each prepared tooth was digitized and evaluated in terms of occlusal reduction and total occlusal convergence using SAE and DAE. Intra-rater agreement for the scores graded by the SAE and DAE and inter-rater agreement between the SAE and DAE were analyzed with the significance level (α) of 0.05. The evaluation using the SAE protocol demonstrated perfect intra-rater agreement, whereas the evaluation using the DAE protocol showed moderate-to-good intra-rater agreement. The evaluation values of the SAE and DAE protocols showed almost perfect inter-rater agreement. The SAE developed for tooth preparation evaluation can be used for dental education and clinical skill feedback. SAE may minimize possible errors in the conventional rating and provide more reliable and precise assessments than the human-based DAE.

The Clinical Potential of 3D-Printed Crowns Reinforced with Zirconia and Glass Silica Microfillers

The development of 3D-printed crown resin materials with improved mechanical and physical properties is an area of growing interest in dentistry. This study aimed to develop a 3D-printed crown resin material modified with zirconia glass (ZG) and glass silica (GS) microfillers to enhance overall mechanical and physical properties. A total of 125 specimens were created and divided into five groups: control unmodified resin, 5% either ZG or GS reinforced 3D-printed resin, and 10% either ZG or GS reinforced 3D-printed resin. The fracture resistance, surface roughness, and translucency parameter were measured, and fractured crowns were studied under a scanning electron microscope. The results showed that 3D-printed parts that were strengthened with ZG and GS microfillers demonstrated comparable mechanical performance to unmodified crown resin but resulted in greater surface roughness, and only the group that contained 5% ZG showed an increase in translucency. However, it should be noted that increased surface roughness may impact the aesthetics of the crowns, and further optimisation of microfillers concentrations may be necessary. These findings suggest that the newly developed dental-based resins that incorporate microfillers could be suitable for clinical applications, but further studies are necessary to optimise the nanoparticle concentrations and investigate their long-term clinical outcomes.