Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review

Can deep learning-designed anterior tooth-borne crown fulfill morphologic, aesthetic, and functional criteria in clinical practice?

OBJECTIVES

This study aimed to compare the design outcomes of anterior crowns generated using deep learning (DL)-based software with those fabricated by a technician using conventional dental computer-assisted design (CAD) software without DL support, with a focus on the evaluation of crown morphology, function, and aesthetics.

METHODS

Twenty-five in vivo datasets comprising maxillary and mandibular arch scans of prepared maxillary central incisors were utilized to design anterior crowns by using three methods: 1) a DL-based method resulting in as-generated outcome (DB), 2) a DL-based method further optimized by a technician (DM), and 3) a conventional CAD-based method (NC, control). Evaluations were conducted for crown morphology (total discrepancy volume (TDV), root mean square (RMS), positive average (PA) and negative average (NA) deviations), functional aspects (incisal path: deviations, length, and mean inclination), and aesthetics (crown width, height, width-to-height ratio, angular radius of mesioincisal line angle, proximal contact length, and tooth axis angle).

RESULTS

Significant differences in TDV ratio were noted between the DB-NC (32.3 ± 8.5 %) and DM-NC (26.5 ± 5.4 %) pairs (P = 0.006). No significant differences were observed in TDV between the DB-NC (65.3 ± 24.4 mm3) and DM-NC (54.3 ± 21.0 mm3) pairs (P = 0.095). For the entire palatal surface, significant differences in RMS and PA values were observed between the DB-NC and DM-NC pairs (P < 0.037). Significant differences in RMS values for the incisal half (P = 0.021) and in PA values for the cervical half (P = 0.047) of the palatal surface were also noted between these pairs. Significant differences in the deviation of the incisal path were observed between the DB-NC (290.4 ± 212.4 μm) and DM-NC (132.0 ± 122.3 μm) pairs (P < 0.001). However, no significant differences were found among the groups (DB, DM, and NC) in terms of the length and mean inclination of incisal paths or in aesthetic outcomes.

CONCLUSIONS

A DL-based method can result in promising outcomes with clinically acceptable morphology and aesthetics for anterior crowns. Minor deviations in incisal path of the crowns may lead to anterior guidance discrepancies, which can be corrected by the dental technician at the design stage.

CLINICAL SIGNIFICANCE

With the potential of DL-based design methods in dental applications, integrating AI technology into dental CAD workflow can enhance the clinical efficiency and consistency of anterior crown design, although human intervention may be required to refine functional aspect.

Feasibility of using two generative AI models for teeth reconstruction

OBJECTIVES

This feasibility study investigates the application of artificial intelligence (AI) models, specifically transformer-based (TM) and diffusion-based (DM) models, for the reconstruction of single and multiple missing teeth.

METHODS

A dataset of 129 digitized models was utilized to create both TM and DM models. Single and multiple missing teeth were artificially generated. Reconstruction accuracy was assessed against ground truth data using Root Mean Square (RMS) and mean absolute error (MAE) across various artificially generated teeth. Paired t-tests were used for analyzing differences between the two models (p < 0.05).

RESULTS

Both TM and DM models demonstrated similar accuracy in the reconstruction of single and multiple missing teeth. The greatest disparity occurred in the reconstruction of all remaining teeth, with the exception of 33 and 43 for both models (RMS TM: 0.37; DM: 0.43). TM exhibited the highest precision in reconstructing tooth 34 (RMS: 0.21), whereas DM demonstrated superior accuracy in reconstructing tooth 21 (RMS: 0.19). Despite there was no significant difference between the models.

CONCLUSIONS

AI-based TM and DM models demonstrate promising results in reconstructing missing teeth, with superior accuracy in single-tooth compared to multiple-tooth edentulous spaces. Despite the need for additional refining and larger datasets, including antagonistic teeth, these models have the potential to streamline and improve the dental restoration processes, potentially leading to cost savings and enhanced clinical outcomes.

SIGNIFICANCE

This study demonstrates the feasibility and potential of transformer- and diffusion-based AI models to accurately reconstruct missing teeth, offering a novel approach that could streamline and enhance the precision of implant planning.

Artificial neural networks development in prosthodontics - a systematic mapping review

OBJECTIVES

This study aimed to systematically categorize the available literature and offer a comprehensive overview of artificial neural network (ANN) prediction models in prosthodontics. Specifically, the present research introduces a systematic analysis of ANN aims, data, architectures, evaluation metrics, and limitations in prosthodontics.

DATA

The review included articles published until June 2024. The search terms included "prosthodontics" (and related MeSH terms), "neural networks", and "deep learning". Out of 597 identified articles, 70 reports remained after deduplication and screening (2007-2024). Of these, 33 % were from 2023. Implant prosthodontics was the focus in approximately 29 % of reports, and non-implant prosthodontics in 71 %.

SOURCES

Data were collected through electronic searches of PubMed MedLine, PubMed Central, ScienceDirect, Web of Science, and IEEE Xplore databases, along with manual searches in specific journals.

STUDY SELECTION

This study focused on English-language research articles and conference proceedings detailing the development and implementation of ANN prediction models specifically designed for prosthodontics.

CONCLUSIONS

This study shows how ANN models are used in implant and non-implant prosthodontics, with various types of data, architectures, and metrics used for their development and evaluation. It also reveals limitations in ANN development, particularly in the data lifecycle.

CLINICAL SIGNIFICANCE

This study equips practitioners with insights, guiding them in optimizing clinical protocols through ANN integration and facilitating informed decision-making on commercially available systems. Additionally, it supports regulatory efforts, smoothing the path for AI integration in dentistry. Moreover, it sets a trajectory for future exploration, identifying untapped tools and research avenues, fostering interdisciplinary collaborations, and driving innovation in the field.

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.

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.

Advancing maxillofacial prosthodontics by using pre-trained convolutional neural networks: Image-based classification of the maxilla

PURPOSE

The study aimed to compare the performance of four pre-trained convolutional neural networks in recognizing seven distinct prosthodontic scenarios involving the maxilla, as a preliminary step in developing an artificial intelligence (AI)-powered prosthesis design system.

MATERIALS AND METHODS

Seven distinct classes, including cleft palate, dentulous maxillectomy, edentulous maxillectomy, reconstructed maxillectomy, completely dentulous, partially edentulous, and completely edentulous, were considered for recognition. Utilizing transfer learning and fine-tuned hyperparameters, four AI models (VGG16, Inception-ResNet-V2, DenseNet-201, and Xception) were employed. The dataset, consisting of 3541 preprocessed intraoral occlusal images, was divided into training, validation, and test sets. Model performance metrics encompassed accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and confusion matrix.

RESULTS

VGG16, Inception-ResNet-V2, DenseNet-201, and Xception demonstrated comparable performance, with maximum test accuracies of 0.92, 0.90, 0.94, and 0.95, respectively. Xception and DenseNet-201 slightly outperformed the other models, particularly compared with InceptionResNet-V2. Precision, recall, and F1 scores exceeded 90% for most classes in Xception and DenseNet-201 and the average AUC values for all models ranged between 0.98 and 1.00.

CONCLUSIONS

While DenseNet-201 and Xception demonstrated superior performance, all models consistently achieved diagnostic accuracy exceeding 90%, highlighting their potential in dental image analysis. This AI application could help work assignments based on difficulty levels and enable the development of an automated diagnosis system at patient admission. It also facilitates prosthesis designing by integrating necessary prosthesis morphology, oral function, and treatment difficulty. Furthermore, it tackles dataset size challenges in model optimization, providing valuable insights for future research.

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.

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.

Recent Advances in Digital Technology in Implant Dentistry

Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.

Artificial intelligence systems in dental shade-matching: A systematic review

PURPOSE

Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade-matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluate the accuracy of artificial intelligence in predicting dental shades in restorative dentistry.

METHODS

A systematic electronic search was performed with the databases MEDLINE (PubMed), Scopus, Cochrane Library, and Google Scholar. A manual search was also conducted. All titles and abstracts were subject to the inclusion criteria of observational, interventional studies, and studies published in the English language. Narrative reviews, systematic reviews, case reports, case series, letters to the editor, commentaries, studies that were not AI-based, studies that were not related to dentistry, and studies that were related to other disciplines in dentistry, other than restorative dentistry (prosthodontics and endodontics) were excluded. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (non-randomized experimental studies). A third investigator was consulted to resolve the lack of consensus.

RESULTS

Fifty-three articles were initially found from all the searches combined from articles published from 2008 till March 2023. A total of 15 articles met the inclusion criteria and were included in the systematic review. AI algorithms for shade-matching include fuzzy logic, a genetic algorithm with back-propagation neural network, back-propagation neural networks, convolutional neural networks, artificial neural networks, support vector machine algorithms, K-nearest neighbor with decision tree and random forest, deep learning for detection of dental prostheses based on object-detection applications, You Only Look Once-YOLO. Moment invariant was used for feature extraction.  XG (Xtreme Gradient) Boost was used in one study as a gradient-boosting machine learning algorithm. The highest accuracy in the prediction of dental shades was the decision tree regression model for leucite-based dental ceramics of 99.7% followed by the fuzzy decision of 99.62%, and support vector machine using cross-validation of 97%.

CONCLUSIONS

Lighting conditions, shade-matching devices and color space models, and the type of AI algorithm influence the accuracy of the prediction of dental shades. Knowledge-based systems and neural networks have shown better accuracy in predicting dental shades.

Taxonomic discordance of immersive realities in dentistry: A systematic scoping review

OBJECTIVES

This review aimed to map taxonomy frameworks, descriptions, and applications of immersive technologies in the dental literature.

DATA

The Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines was followed, and the protocol was registered at open science framework platform (https://doi.org/10.17605/OSF.IO/H6N8M).

SOURCES

Systematic search was conducted in MEDLINE (via PubMed), Scopus, and Cochrane Library databases, and complemented by manual search.

STUDY SELECTION

A total of 84 articles were included, with 81 % between 2019 and 2023. Most studies were experimental (62 %), including education (25 %), protocol feasibility (20 %), in vitro (11 %), and cadaver (6 %). Other study types included clinical report/technique article (24 %), clinical study (9 %), technical note/tip to reader (4 %), and randomized controlled trial (1 %). Three-quarters of the included studies were published in oral and maxillofacial surgery (38 %), dental education (26 %), and implant (12 %) disciplines. Methods of display included head mounted display device (HMD) (55 %), see through screen (32 %), 2D screen display (11 %), and projector display (2 %). Descriptions of immersive realities were fragmented and inconsistent with lack of clear taxonomy framework for the umbrella and the subset terms including virtual reality (VR), augmented reality (AR), mixed reality (MR), augmented virtuality (AV), extended reality, and X reality.

CONCLUSIONS

Immersive reality applications in dentistry are gaining popularity with a notable surge in the number of publications in the last 5 years. Ambiguities are apparent in the descriptions of immersive realities. A taxonomy framework based on method of display (full or partial) and reality class (VR, AR, or MR) is proposed.

CLINICAL SIGNIFICANCE

Understanding different reality classes can be perplexing due to their blurred boundaries and conceptual overlapping. Immersive technologies offer novel educational and clinical applications. This domain is fast developing. With the current fragmented and inconsistent terminologies, a comprehensive taxonomy framework is necessary.

Influence of print orientation and wet-dry storage time on the intaglio accuracy of additively manufactured occlusal devices

STATEMENT OF PROBLEM

Different factors can affect the manufacturing accuracy of additively manufactured dental devices; however, the influence of print orientation and wet-dry storage time on their intaglio accuracy remains uncertain.

PURPOSE

The purpose of this in vitro study was to assess the effect of print orientation (0, 45, 70, and 90 degrees) and wet-dry storage time (0, 30, 60, and 90 days) on the intaglio accuracy of additively manufactured occlusal devices.

MATERIAL AND METHODS

An occlusal device design was obtained in a standard tessellation language (STL) file format (control file) which was used to fabricate all the specimens by using a stereolithography printer (Form 3+) and a biocompatible resin material (Dental LT Clear Resin, V2). Four groups were created based on the print orientation used to manufacture the specimens: 0, 45, 70, and 90 degrees. Each group was divided into 4 subgroups depending on the time elapsed between manufacturing and accuracy evaluation: 0, 30, 60, and 90 days. For the subgroup 0, a desktop scanner (T710) was used to digitize all the specimens. The 30-day subgroup specimens were stored for 30 days with the following daily storage protocol: 16 hours inside a dry lightproof container, followed by 8 hours in artificial saliva (1700-0305 Artificial Saliva) inside the same lightproof container. The specimens were then digitized by following the same procedures used for subgroup 0. For the subgroups 60 and 90, the identical procedures described for subgroup 30 were completed but after 60 and 90 days of storage, respectively. The reference STL file was used to measure the intaglio discrepancy with the experimental scans obtained among the different subgroups by using the root mean square error calculation. Two-way ANOVA and post hoc Tukey pairwise comparison tests were used to analyze the data (α=.05).

RESULTS

Print orientation (P<.001) and usage time (P<.001) were significant predictors of the trueness value obtained. Additionally, the 0-degree print orientation at day 0 group demonstrated the best trueness value among all the groups tested (P<.05). No significant trueness discrepancies were found among the 45-, 70-, and 90-degree print orientation, or among the 30, 60, and 90 days of storage. A significant precision difference was found in the variance between print orientation groups across usage time subgroups.

CONCLUSIONS

The print orientation and wet-dry storage times tested influenced the trueness and precision of the intaglio surfaces of the occlusal devices manufactured with the 3D printer and material selected.

Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study

STATEMENT OF PROBLEM

Computer-aided design and computer-aided manufacturing (CAD-CAM) technology has greatly improved the efficiency of the fabrication of dental prostheses. However, the design process (CAD stage) is still time-consuming and labor intensive.

PURPOSE

The purpose of this feasibility study was to investigate the accuracy of a novel artificial intelligence (AI) system in designing biomimetic single-molar dental prostheses by comparing and matching them to the natural molar teeth.

MATERIAL AND METHODS

A total of 169 maxillary casts were obtained from healthy dentate participants. The casts were digitized, duplicated, and processed with the removal of the maxillary right first molar. A total of 159 pairs of original and processed casts were input into the Generative Adversarial Networks (GANs) for training. In validation, 10 sets of processed casts were input into the AI system, and 10 AI-designed teeth were generated through backpropagation. Individual AI-designed teeth were then superimposed onto each of the 10 original teeth, and the morphological differences in mean Hausdorff distance were measured. True reconstruction was defined as correct matching between the AI-designed and original teeth with the smallest mean Hausdorff distance. The ratio of true reconstruction was calculated as the Intersection-over-Union. The reconstruction performance of the AI system was determined by the Hausdorff distance and Intersection-over-Union.

RESULTS

Data of validation showed that the mean Hausdorff distance ranged from 0.441 to 0.752 mm and the Intersection-over-Union of the system was 0.600 (60%).

CONCLUSIONS

This study demonstrated the feasibility of AI in designing single-molar dental prostheses. With further training and optimization of algorithms, the accuracy of biomimetic AI-designed dental prostheses could be further enhanced.

An overview of the different digital facebow methods for transferring the maxillary cast into the virtual articulator

OBJECTIVES

The purposes of this study were to classify the described digital facebow techniques for transferring the maxillary cast into the semi-adjustable virtual articulator based on the digital data acquisition technology used and to review the reported accuracy values of the different digital facebow methods described.

OVERVIEW

Digital data acquisition technologies, including digital photographs, facial scanners, cone beam computed tomography (CBCT) imaging, and jaw tracking systems, can be used to transfer the maxillary cast into the virtual articulator. The reported techniques are reviewed, as well as the reported accuracy values of the different digital facebow methods.

CONCLUSIONS

Digital photographs can be used to transfer the maxillary cast into the virtual articulator using the true horizontal reference plane, but limited studies have assessed the accuracy of this method. Facial scanning and CBCT techniques can be used to transfer the maxillary cast into the virtual articulator, in which the most frequently selected references planes are the Frankfort horizontal, axis orbital, and true horizontal planes. Studies analyzing the accuracy of the maxillary cast transfer by using facial scanning and CBCT techniques are restricted. Lastly, optical jaw trackers can be selected for transferring the maxillary cast into the virtual articulator by using the axis orbital or true horizontal planes, yet the accuracy of these systems is unknown.

CLINICAL IMPLICATIONS

Digital data acquisition technologies, including digital photographs, facial scanning methods, CBCTs, and optical jaw tracking systems, can be used to transfer the maxillary cast into the virtual articulator. Studies are needed to assess the accuracy of these digital data acquisition technologies for transferring the maxillary cast into the virtual articulator.

An overview of artificial intelligence based applications for assisting digital data acquisition and implant planning procedures

OBJECTIVES

To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures.

OVERVIEW

A review of the main AI-based applications integrated into digital data acquisitions technologies (facial scanners (FS), intraoral scanners (IOSs), cone beam computed tomography (CBCT) devices, and jaw trackers) and computer-aided static implant planning programs are provided.

CONCLUSIONS

The main AI-based application integrated in some FS's programs involves the automatic alignment of facial and intraoral scans for virtual patient integration. The AI-based applications integrated into IOSs programs include scan cleaning, assist scanning, and automatic alignment between the implant scan body with its corresponding CAD object while scanning. The more frequently AI-based applications integrated into the programs of CBCT units involve positioning assistant, noise and artifacts reduction, structures identification and segmentation, airway analysis, and alignment of facial, intraoral, and CBCT scans. Some computer-aided static implant planning programs include patient's digital files, identification, labeling, and segmentation of anatomical structures, mandibular nerve tracing, automatic implant placement, and surgical implant guide design.

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.

Suitability and Trueness of the Removable Partial Denture Framework Fabricating by Polyether Ether Ketone with CAD-CAM Technology

The object of the study was to evaluate the suitability and trueness of the removable partial denture (RPD) framework fabricated by polyether ether ketone (PEEK) with the CAD-CAM technology in vitro. Four different types of dentition defects were selected. In each type, five PEEK RPD frameworks were fabricated by the CAD-CAM technology, while five Co-Cr RPD frameworks were made by traditional casting. The suitability of the framework was evaluated by silicone rubber film slice measurement and the three-dimensional image overlay method. The trueness of the PEEK framework was detected by the three-dimensional image overlay method. Data were statistically analyzed with the use of an independent samples t-test (α = 0.05). The suitability values by silicone rubber film slice measurement of the PEEK group were lower than those of the Co-Cr group in four types, with the differences indicating statistical significance (p < 0.05) in type one, type two, and type four. The suitability values using the three-dimensional image overlay method showed no statistical differences (p > 0.05) between the two groups in four types. The trueness values of the PEEK group were within the allowable range of clinical error. The suitability and trueness of the PEEK RPD framework fabricated by CAD-CAM technology met the requirements of the clinical prosthesis.

Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs

PURPOSE

The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability.

MATERIALS AND METHODS

A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05).

RESULTS

The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated.

CONCLUSIONS

Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement.

CLINICAL SIGNIFICANCE

The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.

ChatGPT performance in prosthodontics: Assessment of accuracy and repeatability in answer generation

STATEMENT OF PROBLEM

The artificial intelligence (AI) software program ChatGPT is based on large language models (LLMs) and is widely accessible. However, in prosthodontics, little is known about its performance in generating answers.

PURPOSE

The purpose of this study was to determine the performance of ChatGPT in generating answers about removable dental prostheses (RDPs) and tooth-supported fixed dental prostheses (FDPs).

MATERIAL AND METHODS

Thirty short questions were designed about RDPs and tooth-supported FDP, and 30 answers were generated for each of the questions using ChatGPT-4 in October 2023. The 900 generated answers were independently graded by experts using a 3-point Likert scale. The relative frequency and absolute percentage of answers were described. Accuracy was assessed using the Wald binomial method, while repeatability was evaluated using percentage agreement, Brennan and Prediger coefficient, Conger generalized Cohen kappa, Fleiss kappa, Gwet AC, and Krippendorff alpha methods. Confidence intervals were set at 95%. Statistical analysis was performed using the STATA software program.

RESULTS

The performance of ChatGPT in generating answers related to RDP and tooth-supported FDP was limited. The answers showed a reliability of 25.6%, with a confidence range between 22.9% and 28.6%. The repeatability ranged from substantial to moderate.

CONCLUSIONS

The results show that currently ChatGPT has limited ability to generate answers related to RDPs and tooth-supported FDPs. Therefore, ChatGPT cannot replace a dentist, and, if professionals were to use it, they should be aware of its limitations.

Dental implant placement with immersive technologies: A preliminary clinical report of augmented and mixed reality applications

A preliminary clinical report of implant placements with 2 immersive reality technologies is described: augmented reality with head mounted display and mixed reality with a tablet PC. Both immersive realities are promising and could facilitate innovative dental applications. However, mixed reality requires further development for clinical optimization.

Accuracy of an artificial intelligence-based program for locating the maxillomandibular relationship of scans acquired by using intraoral scanners

STATEMENT OF PROBLEM

An artificial-intelligence (AI) based program can be used to articulate scans in maximum intercuspal position (MIP) or correct occlusal collisions of articulated scans at MIP; however, the accuracy of the AI program determining the MIP relationship is unknown.

PURPOSE

The purpose of the present clinical study was to assess the influence of intraoral scanner (IOS) (TRIOS 5 or i700) and program (IOS or AI-based program) on the accuracy of the MIP relationship.

MATERIAL AND METHODS

Casts of a participant mounted on an articulator were digitized (T710). A maxillary and a mandibular scan of the participant were recorded by using 2 IOSs: TRIOS 5 and i700. The scans were duplicated 15 times. Then, each duplicated pair of scans was articulated in MIP using a bilateral occlusal record. Articulated scans were duplicated and allocated into 2 groups based on the automatic occlusal collisions' correction completed by using the corresponding IOS program: IOS-corrected and IOS-noncorrected group. Three subgroups were created based on the AI-based program (Bite Finder) method: AI-articulated, AI-IOS-corrected, and AI-IOS-noncorrected (n=15). In the AI-articulated subgroup, the nonarticulated scans were imported and articulated. In the AI-IOS-corrected subgroup, the articulated scans obtained in the IOS-corrected group were imported, and the occlusal collisions were corrected. In the AI-IOS-corrected subgroup, the articulated scans obtained in the IOS-noncorrected subgroup were imported, and the occlusal collisions were corrected. A total of 36 interlandmark measurements were calculated on each articulated scan (Geomagic Wrap). The distances computed on the reference scan were used as a reference to calculate the discrepancies with each experimental scan. Nonparametric 2-way ANOVA and pairwise multiple comparison Dwass-Steel-Critchlow-Fligner tests were used to analyze trueness. The general linear model procedure was used to analyze precision (α=.05).

RESULTS

Significant maxillomandibular trueness (P=.003) and precision (P<.001) differences were found among the subgroups. The IOS-corrected and IOS-noncorrected (P<.001) and AI-articulated and IOS-noncorrected subgroups (P=.011) were significantly different from each other. The IOS-corrected and AI-articulated subgroups obtained significantly better maxillomandibular trueness and precision than the IOS-noncorrected subgroups.

CONCLUSIONS

The IOSs tested obtained similar MIP accuracy; however, the program used to articulate or correct occlusal collusions impacted the accuracy of the MIP relationship.

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.

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.

Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis

STATEMENT OF PROBLEM

The evidence regarding the application of artificial intelligence (AI) in identifying dental implant systems is currently inconclusive. The available studies present varying results and methodologies, making it difficult to draw definitive conclusions.

PURPOSE

The purpose of this systematic review with meta-analysis was to comprehensively analyze and evaluate articles that investigate the application of AI in identifying and classifying dental implant systems.

MATERIAL AND METHODS

An electronic systematic review was conducted across 3 databases: MEDLINE/PubMed, Cochrane, and Scopus. Additionally, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies investigating the accuracy of AI-based diagnostic tools on dental radiographs for identifying and classifying dental implant systems and comparing the results with those obtained by expert judges using manual techniques-the search strategy encompassed articles published until September 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included articles.

RESULTS

Twenty-two eligible articles were included in this review. These articles described the use of AI in detecting dental implants through conventional radiographs. The pooled data showed that dental implant identification had an overall accuracy of 92.56% (range 90.49% to 94.63%). Eleven studies showed a low risk of bias, 6 demonstrated some concern risk, and 5 showed a high risk of bias.

CONCLUSIONS

AI models using panoramic and periapical radiographs can accurately identify and categorize dental implant systems. However, additional well-conducted research is recommended to identify the most common implant systems.

Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry-A Narrative Review

The concept of machines learning and acting like humans is what is meant by the phrase "artificial intelligence" (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.

Effectiveness of a 3D simulation tool to teach the designing of metal removable partial dentures: A mixed-method study

INTRODUCTION

Acquiring insights into the framework design of metal-based removable partial dentures (mRPD) is a current challenge in dental education. The aim of the present study was to explore the effectiveness of a novel 3D simulation tool to teach designing mRPD by investigating the learning gain and the acceptance and motivation towards the tool of dental students.

MATERIALS AND METHODS

A 3D tool based on 74 clinical scenarios was developed for teaching the design of mRPD. Fifty-three third year dental students were randomly divided into two groups, with the experimental group (n = 26) having access to the tool during 1 week while the control group (n = 27) had no access. Quantitative analysis was based on a pre- and post-test in order to evaluate the learning gain, technology acceptance and motivation towards using the tool. Moreover, qualitative data was collected by means of an interview and focus group to get additional insights into the quantitative results.

RESULTS

Although the results showed a higher learning gain for students in the experimental condition, the study did not find a significant difference between both conditions based on quantitative results. However, during the focus groups, all students of the experimental group revealed that the 3D tool improved their understanding of mRPD biomechanics. Moreover, survey results revealed that students positively evaluated the perceived usefulness and ease of use of the tool and indicated to have the intention to use the tool in the future. Suggestions were made for a redesign (e.g. creating scenarios themselves) and further implementation of the tool (e.g. analysing the scenarios in pairs or small groups).

CONCLUSION

First results of the evaluation of the new 3D tool for teaching the design framework of mRPD are promising. Further research based on the design-based research methodology is needed to investigate the effects of the redesign on motivation and learning gain.

Discrepancies in the occlusal devices designed by an experienced dental laboratory technician and by 2 artificial intelligence-based automatic programs

STATEMENT OF PROBLEM

Artificial intelligence (AI) models have been developed for different applications, including the automatic design of occlusal devices; however, the design discrepancies of an experienced dental laboratory technician and these AI automatic programs remain unknown.

PURPOSE

The purpose of this in vitro study was to compare the overall, intaglio, and occlusal surface discrepancies of the occlusal device designs completed by an experienced dental laboratory technician and two AI automatic design programs.

MATERIAL AND METHODS

Virtually articulated maxillary and mandibular diagnostic casts were obtained in a standard tessellation language (STL) file format. Three groups were created depending on the operator or program used to design the occlusal devices: an experienced dental laboratory technician (control group) and two AI programs, namely Medit Splints from Medit (Medit group) and Automate from 3Shape A/S (3Shape group) (n=10). To minimize the discrepancies in the parameter designs among the groups tested, the same printing material and design parameters were selected. In the control group, the dental laboratory technician imported the articulated scans into a dental design program (DentalCAD) and designed a maxillary occlusal device. The occlusal device designs were exported in STL format. In the Medit and 3Shape groups, the diagnostic casts were imported into the respective AI programs. The AI programs automatically designed the occlusal device without any further operator intervention. The occlusal device designs were exported in STL format. Among the 10 occlusal designs of the control group, a random design (shuffle deck of cards) was used as a reference file to calculate the overall, intaglio, and occlusal discrepancies in the specimens of the AI groups by using a program (Medit Design). The root mean square (RMS) error was calculated. Kruskal-Wallis, and post hoc Dwass-Steel-Critchlow-Fligner pairwise comparison tests were used to analyze the trueness of the data. The Levene test was used to assess the precision data (α=.05).

RESULTS

Significant overall (P<.001), intaglio (P<.001), and occlusal RMS median value (P<.001) discrepancies were found among the groups. Significant overall RMS median discrepancies were observed between the control and the Medit groups (P<.001) and the control and 3Shape groups (P<.001). Additionally, significant intaglio RMS median discrepancies were found between the control and the Medit groups (P<.001), the Medit and 3Shape groups (P<.001), and the control and 3Shape groups (P=.008). Lastly, significant occlusal RMS median discrepancies were found between the control and the 3Shape groups (P<.001) and the Medit and 3Shape groups (P<.001). The AI-based software programs tested were able to automatically design occlusal devices with less than a 100-µm trueness discrepancy compared with the dental laboratory technician. The Levene test revealed significant overall (P<.001), intaglio (P<.001), and occlusal (P<.001) precision among the groups tested.

CONCLUSIONS

The use of a dental laboratory technique influenced the overall, intaglio, and occlusal trueness of the occlusal device designs obtained. No differences were observed in the precision of occlusal device designs acquired among the groups tested.

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

OBJECTIVE

The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.

OVERVIEW

An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.

CONCLUSIONS

Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.

CLINICAL SIGNIFICANCE

AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

Superimposition of intraoral scans of an edentulous arch with implants and implant-supported provisional restoration, implementing a novel implant prosthetic scan body

Purpose To describe a technique utilizing a novel prosthetic scan body, that assists the accurate merging of multiple scans (intra- and extraoral) of the interim prosthesis and edentulous arch with dental implants, during rehabilitation with a fixed implant-supported prosthesis.Methods Intraoral scanning (Trios 3, 3Shape) of an interim implant-supported prosthesis was performed, subsequently followed by another scan, using five scan bodies, placed onto the implant abutments (SRA, Bone level, Straumann AG). Successively, the newly designed prosthetic scan bodies were attached to the abutment copings of the interim prosthesis, for extraoral scanning. Utilizing an implant library designed for the prosthetic scan body, the three scans were merged, providing all the necessary information for the digital design and fabrication of the fixed implant-supported prosthesis.Conclusions The described clinical technique enabled effective and accurate superimposition of intra- and extraoral scans of the implant prosthesis. Superimposed data, including that of the position of dental implants and anatomy of soft tissue, provided essential information for the fabrication of a definitive implant-supported prosthesis. The novel prosthetic scan bodies attached to the implant prosthesis, assisted in merging intra- and extraoral scans, thus facilitating the rehabilitation of maxillary and/or mandibular edentulous dental arches. Further research is required to assess the accuracy of the proposed technique.

Artificial Intelligence and Its Theranostic Applications in Dentistry

As new technologies emerge, they continue to have an impact on our daily lives, and artificial intelligence (AI) covers a wide range of applications. Because of the advancements in AI, it is now possible to analyse large amounts of data, which results in more accurate data and more effective decision-making. This article explains the fundamentals of AI and examines its development and present use. AI technology has had an impact on the healthcare sector as a result of the need for accurate diagnosis and improved patient care. An overview of the existing AI applications in clinical dentistry was provided. Comprehensive care involving artificial intelligence aims to provide cutting-edge research and innovations, as well as high-quality patient care, by enabling sophisticated decision support tools. The cornerstone of AI advancement in dentistry is creative inter-professional coordination among medical professionals, scientists, and engineers. Artificial intelligence will continue to be associated with dentistry from a wide angle despite potential misconceptions and worries about patient privacy. This is because precise treatment methods and quick data sharing are both essential in dentistry. Additionally, these developments will make it possible for patients, academicians, and healthcare professionals to exchange large data on health as well as provide insights that enhance patient care.

The Complete Digital Workflow in Fixed Prosthodontics Updated: A Systematic Review

Digital applications have changed therapy in prosthodontics. In 2017, a systematic review reported on complete digital workflows for treatment with tooth-borne or implant-supported fixed dental prostheses (FDPs). Here, we aim to update this work and summarize the recent scientific literature reporting complete digital workflows and to deduce clinical recommendations. A systematic search of PubMed/Embase using PICO criteria was performed. English-language literature consistent with the original review published between 16 September 2016 and 31 October 2022 was considered. Of the 394 titles retrieved by the search, 42 abstracts were identified, and subsequently, 16 studies were included for data extraction. A total of 440 patients with 658 restorations were analyzed. Almost two-thirds of the studies focused on implant therapy. Time efficiency was the most often defined outcome (n = 12/75%), followed by precision (n = 11/69%) and patient satisfaction (n = 5/31%). Though the amount of clinical research on digital workflows has increased within recent years, the absolute number of published trials remains low, particularly for multi-unit restorations. Current clinical evidence supports the use of complete digital workflows in implant therapy with monolithic crowns in posterior sites. Digitally fabricated implant-supported crowns can be considered at least comparable to conventional and hybrid workflows in terms of time efficiency, production costs, precision, and patient satisfaction.

Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants

Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient's condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.

Influence of the layer thickness on the flexural strength of aged and non-aged additively manufactured interim dental material

PURPOSE

To measure the flexural strength and Weibull characteristics of aged and non-aged printed interim dental material fabricated with different layer thickness.

MATERIAL AND METHODS

Bars (25×2×2 mm) were additively fabricated by using a polymer printer (Asiga Max) and an interim resin (Nexdent C&B MFH). Specimens were fabricated with the same printing parameters and postprocessing procedures, but with 7 different layer thickness: 50 (control or 50-G group), 10 (10-G group), 25 (25-G group), 75 (75-G group), 100 (100-G group), 125 (125-G group), and 150 μm (150-G group). Two subgroups were created: non-aged and aged subgroups (n = 10). A universal testing machine was selected to measure flexural strength. Two-parameter Weibull distribution values were computed. Two-way ANOVA and Tukey tests were elected to examine the data (α = .05).

RESULTS

Artificial aging methods (P<.001) were a significant predictor of the flexural strength computed. Aged specimens acquired less flexural strength than non-aged specimens. The Weibull distribution obtained the highest shape for non-aged 50-G and 75-G group specimens compared with those of other non-aged groups, while the Weibull distribution showed the highest shape for aged 125-G specimens.

CONCLUSIONS

The flexural strength of the additively fabricated interim material examined was not influenced by the layer thickness at which the specimens were fabricated; however, artificial aging techniques reduced its flexural strength. Aged specimens presented lower Weibull distribution values compared with non-aged specimens, except for the 125-G specimens. This article is protected by copyright. All rights reserved.

Smile Design: Mechanical Considerations

Smile designing refers to the cosmetic and esthetic dental reconstruction that is visible during smiling. The use of modern digital tools requires adequate knowledge about the tooth shape and shade principles. Mechanical, biological, and psychological factors should be understood and tailor an individualized treatment accordingly to achieve pleasing esthetic outcomes. Dental therapy is becoming more appearance-driven, and thus, both patients and dental clinicians mainly emphasize on cosmetic dental and facial aspects of treatments.

Artificial intelligence-designed single molar dental prostheses: A protocol of prospective experimental study

BACKGROUND

Dental prostheses, which aim to replace missing teeth and to restore patients' appearance and oral functions, should be biomimetic and thus adopt the occlusal morphology and three-dimensional (3D) position of healthy natural teeth. Since the teeth of an individual subject are controlled by the same set of genes (genotype) and are exposed to mostly identical oral environment (phenotype), the occlusal morphology and 3D position of teeth of an individual patient are inter-related. It is hypothesized that artificial intelligence (AI) can automate the design of single-tooth dental prostheses after learning the features of the remaining dentition.

MATERIALS AND METHODS

This article describes the protocol of a prospective experimental study, which aims to train and to validate the AI system for design of single molar dental prostheses. Maxillary and mandibular dentate teeth models will be collected and digitized from at least 250 volunteers. The (original) digitized maxillary teeth models will be duplicated and processed by removal of right maxillary first molars (FDI tooth 16). Teeth models will be randomly divided into training and validation sets. At least 200 training sets of the original and the processed digitalized teeth models will be input into 3D Generative Adversarial Network (GAN) for training. Among the validation sets, tooth 16 will be generated by AI on 50 processed models and the morphology and 3D position of AI-generated tooth will be compared to that of the natural tooth in the original maxillary teeth model. The use of different GAN algorithms and the need of antagonist mandibular teeth model will be investigated. Results will be reported following the CONSORT-AI.

Personalized workflows in reconstructive dentistry-current possibilities and future opportunities

OBJECTIVES

The increasing collection of health data coupled with continuous IT advances have enabled precision medicine with personalized workflows. Traditionally, dentistry has lagged behind general medicine in the integration of new technologies: So what is the status quo of precision dentistry? The primary focus of this review is to provide a current overview of personalized workflows in the discipline of reconstructive dentistry (prosthodontics) and to highlight the disruptive potential of novel technologies for dentistry; the possible impact on society is also critically discussed.

MATERIAL AND METHODS

Narrative literature review.

RESULTS

Narrative literature review.

CONCLUSIONS

In the near future, artificial intelligence (AI) will increase diagnostic accuracy, simplify treatment planning, and thus contribute to the development of personalized reconstructive workflows by analyzing e-health data to promote decision-making on an individual patient basis. Dental education will also benefit from AI systems for personalized curricula considering the individual students' skills. Augmented reality (AR) will facilitate communication with patients and improve clinical workflows through the use of visually guided protocols. Tele-dentistry will enable opportunities for remote contact among dental professionals and facilitate remote patient consultations and post-treatment follow-up using digital devices. Finally, a personalized digital dental passport encoded using blockchain technology could enable prosthetic rehabilitation using 3D-printed dental biomaterials.

CLINICAL SIGNIFICANCE

Overall, AI can be seen as the door-opener and driving force for the evolution from evidence-based prosthodontics to personalized reconstructive dentistry encompassing a synoptic approach with prosthetic and implant workflows. Nevertheless, ethical concerns need to be solved and international guidelines for data management and computing power must be established prior to a widespread routine implementation.