Dentronics: Towards robotics and artificial intelligence in dentistry

Metaverse technology tree: a holistic view

Introduction

The Metaverse has emerged as a significant trend in recent years, offering solutions across diverse fields. Despite substantial investments and extensive research efforts, a comprehensive understanding of the Metaverse environment and its full potential remains elusive. This article seeks to address this gap by developing a technology tree for the Metaverse based on published standards, prior studies, and frameworks proposed by leading firms.

Methods

To construct the Metaverse technology tree, a systematic literature review approach was employed. From an initial pool of 354 scientific papers, conference proceedings, book chapters, and reports, a rigorous screening process -focused on titles, abstracts, and full-texts -resulted in a selection of 81 final sources. These sources were synthesized using a meta-analysis methodology.

Results

The meta-synthesis of the selected literature produced a comprehensive Metaverse technology tree encompassing seven key branches: artificial intelligence, Mirror World, extended reality, network infrastructure, lifelogging, blockchain, and the Internet of Things. Each branch represents a critical technological area necessary for the development and realization of the Metaverse.

Discussion

The proposed Metaverse technology tree offers a holistic overview and roadmap of the technological domains underlying the Metaverse. By identifying these seven branches, this research provides valuable guidance for future studies and development trajectories in Metaverse technologies.

Application of machine learning in dentistry: insights, prospects and challenges

BACKGROUND

Machine learning (ML) is transforming dentistry by setting new standards for precision and efficiency in clinical practice, while driving improvements in care delivery and quality.

OBJECTIVES

This review: (1) states the necessity to develop ML in dentistry for the purpose of breaking the limitations of traditional dental technologies; (2) discusses the principles of ML-based models utilised in dental clinical practice and care; (3) outlines the application respects of ML in dentistry; and (4) highlights the prospects and challenges to be addressed.

DATA AND SOURCES

In this narrative review, a comprehensive search was conducted in PubMed/MEDLINE, Web of Science, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases.  Conclusions: Machine Learning has demonstrated significant potential in dentistry with its intelligently assistive function, promoting diagnostic efficiency, personalised treatment plans and related streamline workflows. However, challenges related to data privacy, security, interpretability, and ethical considerations were highly urgent to be addressed in the next review, with the objective of creating a backdrop for future research in this rapidly expanding arena.  Clinical significance: Development of ML brought transformative impact in the fields of dentistry, from diagnostic, personalised treatment plan to dental care workflows. Particularly, integrating ML-based models with diagnostic tools will significantly enhance the diagnostic efficiency and precision in dental surgeries and treatments.

Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography

Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improve segmentation accuracy of dental anatomies, including the maxillary sinuses, maxilla, mandible, and inferior alveolar nerves (IAN), which are important for implant planning, in 3D cone-beam computed tomography (CBCT) scans. Segmentation accuracy was compared using single-label, adjacent pair-label, and multi-label relevant anatomic structures with 60 CBCT scans from Kooalldam Dental Hospital and externally validated using data from Seoul National University Dental Hospital. The dataset was divided into three training stages for active learning. The evaluation metrics were assessed through the Dice similarity coefficient (DSC) and mean absolute difference. The overall internal test set DSCs from the multi-label, single-label, and pair-label models were 95%, 91% (paired t-test; p = 0.01), and 93% (p = 0.03), respectively. The DSC of the IAN in the internal and external datasets increased from 83% to 79%, 87% and 81%, to 90% and 86% for the single-label, pair-label, and multi-label models, respectively (all p = 0.01). Prediction accuracy improved over time, significantly reducing the manual correction time. Our active learning and multi-label strategies facilitated accurate automatic segmentation.

Implant placement with an autonomous dental implant robot: A clinical report

Ideal implant placement is the basis for long-term implant survival and satisfactory restoration outcomes. Static and dynamic computer-assisted guidance have been used to improve the accuracy of implant placement, but both have shortcomings that robots can overcome. This clinical report describes the use of an autonomous implant robot to complete the placement of 2 adjacent implants with immediate postoperative restoration.

Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment

BACKGROUND AND OBJECTIVES

If left untreated, cracked teeth can lead to tooth loss, of which the incidence is 70%. Dental preparation is an effective treatment, but it is difficult to meet the clinical requirements when traditionally prepared by dentists. Grinding-based tooth preparation robot (TPR) shows promise for clinical applications to assist dentists. However, current TPR has problems with chipping and crack extension when preparing real teeth.

METHODS

We propose a grinding parameter optimization strategy to solve this problem, specifically including preparation depth and direction. Among them, surface morphology observation technology and thermal-mechanical coupling simulation technology are used. Through theoretical modeling, computer simulation techniques and surface morphology experimental studies, different motion parameters are compared and analyzed to derive the optimal preparation parameters.

RESULTS

One of our contributions is to control the preparation depth based on the different material removal methods, and the brittle removal methods and grinding heat during the preparation process were reduced. Another contribution is to derive the stress intensity factor (SIF) at the crack tip for different preparation directions based on multi-grit and thermal-mechanical coupling finite element model for different preparation stages. The preparation direction was directed and crack extension was minimized. Finally, the experimental system of the TPR was constructed. Based on the proposed morphology and preparation direction optimization method, the material removal method during the preparation process can be controlled in plastic removal. Crack extension was also reduced based on different stages of optimized preparation directions. Based on the guided strategy, the TPR can provide safe assisted dentists.

CONCLUSIONS

In this work, the preparation parameters of the cracked preparation robot were optimized to enable it to perform the preparation of hard and brittle cracked teeth. The surface morphology met the clinical requirements. Intraoral preparation will be considered in the future to advance the robot toward clinical dental applications.

Artificial intelligence and mixed reality for dental implant planning: A technical note

AIM

The aim of this work is to present a new protocol for implant surgical planning which involves the combined use of artificial intelligence (AI) and mixed reality (MR).

METHODS

This protocol involves the acquisition of three-dimensional (3D) patient data through intraoral scanning (IOS) and cone beam computed tomography (CBCT). These data are loaded into AI software which automatically segments and aligns the patient's 3D models. These 3D models are loaded into MR software and used for planning implant surgery through holography. The files are then exported and used to design surgical guides via open-source software, which are 3D printed and used to prepare the implant sites through static computer-assisted implant surgery (s-CAIS). The case is finalized prosthetically through a fully digital protocol. The accuracy of implant positioning is verified by comparing the planned position with the actual position of the implants after surgery.

RESULTS

As a proof of principle, the present protocol seems to be to be reliable and efficient when used for planning simple cases of s-CAIS in partially edentulous patients. The clinician can plan the implants in an authentic 3D environment without using any radiology-guided surgery software. The precision of implant placement seems clinically acceptable, with minor deviations.

CONCLUSIONS

The present study suggests that AI and MR technologies can be successfully used in s-CAIS for an authentic 3D planning. Further clinical studies are needed to validate this protocol.

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.

The Impact of Medical Explainable Artificial Intelligence on Nurses' Innovation Behaviour: A Structural Equation Modelling Approach

Aim: This study aims to investigate the influence of medical explainable artificial intelligence (XAI) on the innovation behaviour of nurses, as well as explore the dual-pathway mediating effect of AI self-efficacy and AI anxiety and organizational ethical climate as the moderating effect. Background: To address the practical application of medical AI technology, alleviate the scarcity of medical resources and fulfil the medical and health demands of the public, it is crucial to improve the innovation behaviour of nurses through the use of medical XAI. Methods: A cross-sectional survey was conducted involving 368 Chinese nurses working at tertiary and secondary hospitals in Anhui Province, Jiangsu Province, Zhejiang Province and Shanghai. Results: Implementing medical XAI significantly enhanced the innovation behaviour of nurses. Anxiety and self-efficacy regarding AI mediated the connection between medical XAI and the innovation behaviour of nurses. Furthermore, the organizational ethical climate positively moderated the relationship between medical XAI and AI self-efficacy. Conclusion: Medical XAI helps to enhance nurses' AI self-efficacy and reduce AI anxiety, thereby enhancing nurses' innovation behaviour. An organizational ethical climate enhances the positive relationship between medical XAI and AI self-efficacy. Implications for Nursing Management: Organizations and technology developers must augment the study about XAI and the system design of human-centred AI technology. The organizations aim to enhance the education and training of nurses in AI, specifically focussing on boosting nurses' self-efficacy in utilizing AI technology. Moreover, they want to alleviate nurses' fear of new technological advancements. Hospital administrators and leaders develop strategies to address the ethical atmosphere inside their organization.

AI-Driven Innovations in Pediatric Dentistry: Enhancing Care and Improving Outcome

Artificial intelligence (AI) is transforming pediatric dentistry by enhancing diagnostic accuracy, streamlining treatment planning, and improving behavior management. This review explores current AI applications in detecting dental anomalies, categorizing fissure sealants, assessing chronological age, and managing patient behavior. The review also identifies emerging trends and future directions in AI technology that promise to further revolutionize pediatric dental care. By synthesizing recent research and clinical studies, this review aimed to inform dental professionals and researchers about the potential of AI to address traditional challenges and improve oral health outcomes for children.

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Development and accuracy assessment of a crown lengthening surgery robot for use in the esthetic zone: An in vitro study

STATEMENT OF PROBLEM

Crown lengthening surgery has been widely used to enhance the health and esthetics of anterior teeth, and its accuracy significantly influences surgical outcomes. However, the feasibility and accuracy of a robot system for crown lengthening surgery remains unknown.

PURPOSE

The purpose of this in vitro study was to develop a crown lengthening surgery robot and evaluate its accuracy.

MATERIAL AND METHODS

A robotic crown lengthening surgery system consisting of a robotic arm, a robotic software system, and an optical tracking device was designed. Intraoral scanning and cone beam computed tomography (CBCT) were performed on 18 artificial dentition models. The data were imported into the planning software program to synthesize a surgical path for gingivectomy and alveolectomy. Subsequently, a robotic arm equipped with a high-speed handpiece was used to perform these surgical procedures. Postoperatively, the models were rescanned for evaluation, with the accuracy (trueness ±precision) of the surgical outcomes of gingivectomy and alveolectomy being assessed from the trajectories in the highest, lowest, and overall regions. Differences between groups were analyzed by using the independent sample t test and the Levene test (α=.05).

RESULTS

Crown lengthening surgery was feasible in vitro using the robot developed in this study. The overall robot-assisted crown lengthening surgery accuracy (trueness ±precision) of gingivectomy (0.23 ±0.08 mm) was significantly higher than that of alveolectomy (0.33 ±0.11 mm) (P<.05).

CONCLUSIONS

Robot-assisted crown lengthening surgery had acceptable accuracy generally and can be considered a feasible treatment option.

Transforming Prosthodontics and oral implantology using robotics and artificial intelligence

The current review focuses on how artificial intelligence (AI) and robotics can be applied to the field of Prosthodontics and oral implantology. The classification and methodologies of AI and application of AI and robotics in various aspects of Prosthodontics is summarized. The role of AI has potentially expanded in dentistry. It plays a vital role in data management, diagnosis, and treatment planning and administrative tasks. It has widespread applications in Prosthodontics owing to its immense diagnostic capability and possible therapeutic application. AI and robotics are next-generation technologies that are opening new avenues of growth and exploration for Prosthodontics. The current surge in digital human-centered automation has greatly benefited the dental field, as it transforms towards a new robotic, machine learning, and artificial intelligence era. The application of robotics and AI in the dental field aims to improve dependability, accuracy, precision, and efficiency by enabling the widespread adoption of cutting-edge dental technologies in future. Hence, the objective of the current review was to represent literature relevant to the applications of robotics and AI and in the context of diagnosis and clinical decision-making and predict successful treatment in Prosthodontics and oral implantology.

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.

Accuracy of artificial intelligence in implant dentistry: A scoping review with systematic evidence mapping

STATEMENT OF PROBLEM

The use of artificial intelligence (AI) in dentistry has grown. However, the accuracy of clinical applications in implant dentistry is still unclear.

PURPOSE

The purpose of this scoping review with systematic evidence mapping was to identify and describe the available evidence on the accuracy and clinical applications of AI in implant dentistry.

MATERIAL AND METHODS

An electronic search was performed in 4 databases and nonpeer-reviewed literature for articles published up to November 2023. The eligibility criteria comprised observational and interventional studies correlating AI and implant dentistry. A bibliographic mapping and quality analysis of the included studies was conducted. Additionally, the accuracy rates of each AI model were evaluated.

RESULTS

Twenty-six studies met the inclusion criteria. A significant increase in evidence has been observed in recent years. The most commonly found applications of AI in implant dentistry were for the recognition of implant systems followed by surgical implant planning. The performance of AI models was generally high (mean of 88.7%), with marginal bone loss (MBL) prediction models being the most accurate (mean of 93%). Regarding the place of publication, the Asian continent represented the highest number of studies, followed by the European and South American continents.

CONCLUSIONS

Evidence involving AI and implant dentistry has grown in the last decade. Although still under development, all AI models evaluated demonstrated high accuracy and clinical applicability. Further studies evaluating the clinical efficacy of AI models in implant dentistry are essential.

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.

An Investigative Study on the Oral Health Condition of Individuals Undergoing 3D-Printed Customized Dental Implantation

BACKGROUND

The advent of three-dimensional (3D) printing technology has revolutionized the field of dentistry, enabling the precise fabrication of dental implants. By utilizing 3D printing, dentists can devise implant plans prior to surgery and accurately translate them into clinical procedures, thereby eliminating the need for multiple surgical procedures, reducing surgical discomfort, and enhancing surgical efficiency. Furthermore, the utilization of digital 3D-printed implant guides facilitates immediate restoration by precisely translating preoperative implant design plans, enabling the preparation of temporary restorations preoperatively.

METHODS

This comprehensive study aimed to assess the postoperative oral health status of patients receiving personalized 3D-printed implants and investigate the advantages and disadvantages between the 3D-printed implant and conventional protocol. Additionally, variance analysis was employed to delve into the correlation between periodontal status and overall oral health. Comparisons of continuous paired parameters were made by t-test.

RESULTS

The results of our study indicate a commendable one-year survival rate of over 94% for 3D-printed implants. This finding was corroborated by periodontal examinations and follow-up surveys using the Oral Health Impact Profile-14 (OHIP-14) questionnaire, revealing excellent postoperative oral health status among patients. Notably, OHIP-14 scores were significantly higher in patients with suboptimal periodontal health, suggesting a strong link between periodontal health and overall oral well-being. Moreover, we found that the operating time (14.41 ± 4.64 min) was less statistically significant than for the control group (31.76 ± 6.83 min).

CONCLUSION

In conclusion, personalized 3D-printed implant surgery has emerged as a reliable clinical option, offering a viable alternative to traditional implant methods. However, it is imperative to gather further evidence-based medical support through extended follow-up studies to validate its long-term efficacy and safety.

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study

BACKGROUND

Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.

METHODS

A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined.

RESULTS

Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425.

CONCLUSIONS

The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm.

CLINICAL REVELANCE

Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

Clinical application of robots in dentistry: A scoping review

PURPOSE

The surge in digitalization and artificial intelligence has led to the wide application of robots in various fields, but their application in dentistry started relatively late. This scoping review aimed to comprehensively explore and map the current status of the clinical application of robots in dentistry.

STUDY SELECTION

An iterative approach was used to gather as much evidence as possible from four online databases, including PubMed, the China National Knowledge Infrastructure, the Japan Science and Technology Information Aggregator, Electronic, and the Institute of Electrical and Electronics Engineers, from January 1980 to December 2022.

RESULTS

A total of 113 eligible articles were selected from the search results, and it was found that most of the robots were developed and applied in the United States (n = 56; 50%). Robots were clinically applied in oral and maxillofacial surgery, oral implantology, prosthodontics, orthodontics, endodontics, and oral medicine. The development of robots in oral and maxillofacial surgery and oral implantology is relatively fast and comprehensive. About 51% (n = 58) of the systems had reached clinical application, while 49% (n = 55) were at the pre-clinical stage. Most of these are hard robots (90%; n = 103), and their invention and development were mainly focused on university research groups with long research periods and diverse components.

CONCLUSIONS

There are still limitations and gaps between research and application in dental robots. While robotics is threatening to replace clinical decision-making, combining it with dentistry to gain maximum benefit remains a challenge for the future.

Application of Robotics in Orthodontics: A Systematic Review

Robotics has various applications in dentistry, particularly in orthodontics, although the potential use of these technologies is not yet clear. This review aims to summarize the application of robotics in orthodontics and clarify its function and scope in clinical practice. Original articles addressing the application of robotics in any area of orthodontic practice were included, and review articles were excluded. PubMed, Google Scholar, Scopus, and DOAJ were searched from June to August 2023. The risk of bias was established using the risk of bias in non-randomized studies (ROBINS) and certainty assessment tools following the grading of recommendations, assessment, development, and evaluation (GRADE) guidelines. A narrative synthesis of the data was generated and presented according to its application in surgical and non-surgical orthodontics. The search retrieved 2,106 articles, of which 16 articles were selected for final data synthesis of research conducted between 2011 and 2023 in Asia, Europe, and North America. The application of robotics in surgical orthodontics helps guide orthognathic surgeries by reducing the margin of error, but it does not replace the work of a clinician. In non-surgical orthodontics, robotics assists in performing customized bending of orthodontic wires and simulating orthodontic movements, but its application is expensive. The articles collected for this synthesis exhibited a low risk of bias and high certainty, and the results indicated that the advantages of the application of robotics in orthodontics outweigh the disadvantages. This project was self-financed, and a previous protocol was registered at the PROSPERO site (registration number: CRD42023463531).

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

OBJECTIVES

This study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real-time temperature prediction models.

MATERIALS AND METHODS

A combination of in vitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six-axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models.

RESULTS

By incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set.

CONCLUSION

The proof-of-concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot-assisted implant surgery.

[Current Status and Analysis of the Clinical Application of Digital Technology in Oral Medicine]

With the increasing maturity and popularization of digital technology in oral medicine, its application has now expanded to various clinical subspecialties of oral medicine. Digitalization has become one of the important development directions of oral medicine. What is the current development status of digital technology in oral medicine? In what ways is digital technology applied across various clinical specialties of oral medicine? Dentists are particularly concerned about these issues in their clinical work and research. In this paper, all the digital technologies applied in oral medicine are organized and categorized from a technical perspective. In this paper, we focused on presenting three-dimensional data acquisition technology, dental computer-aided design technology, dental computer-aided processing technology, and oral surgery implementation technology. Their technical principles, technical characteristics, applications in oral medicine, a secondary discipline of medicine, and the development status of domestically-developed technology are described and reviewed in detail. The other technologies such as oral digital materials, oral virtual simulation teaching, and oral multi-source data management are briefly discussed. We intend to provide references for dentists to apply digital technology in clinical practice and research.

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.

Role of robotics and artificial intelligence in oral health education. Knowledge, perception and attitude of dentists in India

BACKGROUND

Artificial intelligence or AI may be identified as the use of Personal Computers and/or machines in performing certain specific types of tasks that usually have the requirement of humanized knowledge. These specific tasks include acknowledgment of the problem, understanding disease dynamics, and determining the clinical diagnosis.

MATERIALS AND METHODS

This cross-sectional and prospective study was conducted on Dental professionals who were practicing all across India after obtaining approval from the Institutional Ethical Board. A previously validated as well as pre-analyzed questionnaire form was distributed using electronic mail and through the use of social media with a briefly explained description of the study purpose and an informed consent form. The study questionnaire comprised "close-ended" queries that were then divided into foursections. All the study participants were then instructed to select any one option among all the provided answers. The entire study was completed within one month. Collected observations were entered within a Microsoft Excel 2007® master chart. Statistical analytical software tool SPSS version 20.0, IBM Corporation was employed. "Chi-square" test was performed for evaluating statistical association. A P value lesser than 0.05 was fixed with statistical significance.

RESULTS

On analyzing the level of knowledge, 82.5% of subjects had knowledge of artificial intelligence while 11.4% had no knowledge and 6.1% had some knowledge of this tool. 69.1% were knowledgeable regarding the use of AI in lesional diagnosis, 12.8% had no knowledge regarding artificial intelligence for the diagnosis and 18.1% had no knowledge regarding AI in the diagnosis. 71% had knowledge concerning the use of AI for Imaging. Knowledge of AI in Oral Hygiene was seen in 54.3%. 91.2% of participants had knowledge of robotics use in Oral Surgery. 77% of dentists had knowledge regarding the use of AI for the enhancement of clinical practice. 95.5% had a higher 'positive' attitude toward the use of AI in academics. 69.1% of dentists had a positive attitude regarding AI incorporation in practice. 5% of dentists considered artificial intelligence better than human intelligence for diagnosis. 10% believed that disparities can exist between AI-based and human diagnosis.

CONCLUSION

Positive correlations were noted between knowledge, attitude, and practice of AI among studied dentists.

Ultraviolet disinfection (UV-D) robots: bridging the gaps in dentistry

Maintaining a microbe-free environment in healthcare facilities has become increasingly crucial for minimizing virus transmission, especially in the wake of recent epidemics like COVID-19. To meet the urgent need for ongoing sterilization, autonomous ultraviolet disinfection (UV-D) robots have emerged as vital tools. These robots are gaining popularity due to their automated nature, cost advantages, and ability to instantly disinfect rooms and workspaces without relying on human labor. Integrating disinfection robots into medical facilities reduces infection risk, lowers conventional cleaning costs, and instills greater confidence in patient safety. However, UV-D robots should complement rather than replace routine manual cleaning. To optimize the functionality of UV-D robots in medical settings, additional hospital and device design modifications are necessary to address visibility challenges. Achieving seamless integration requires more technical advancements and clinical investigations across various institutions. This mini-review presents an overview of advanced applications that demand disinfection, highlighting their limitations and challenges. Despite their potential, little comprehensive research has been conducted on the sterilizing impact of disinfection robots in the dental industry. By serving as a starting point for future research, this review aims to bridge the gaps in knowledge and identify unresolved issues. Our objective is to provide an extensive guide to UV-D robots, encompassing design requirements, technological breakthroughs, and in-depth use in healthcare and dentistry facilities. Understanding the capabilities and limitations of UV-D robots will aid in harnessing their potential to revolutionize infection control practices in the medical and dental fields.

Configurations of human-centered AI at work: seven actor-structure engagements in organizations

Purpose

The discourse on the human-centricity of AI at work needs contextualization. The aim of this study is to distinguish prevalent criteria of human-centricity for AI applications in the scientific discourse and to relate them to the work contexts for which they are specifically intended. This leads to configurations of actor-structure engagements that foster human-centricity in the workplace.

Theoretical foundation

The study applies configurational theory to sociotechnical systems' analysis of work settings. The assumption is that different approaches to promote human-centricity coexist, depending on the stakeholders responsible for their application.

Method

The exploration of criteria indicating human-centricity and their synthesis into configurations is based on a cross-disciplinary literature review following a systematic search strategy and a deductive-inductive qualitative content analysis of 101 research articles.

Results

The article outlines eight criteria of human-centricity, two of which face challenges of human-centered technology development (trustworthiness and explainability), three challenges of human-centered employee development (prevention of job loss, health, and human agency and augmentation), and three challenges of human-centered organizational development (compensation of systems' weaknesses, integration of user-domain knowledge, accountability, and safety culture). The configurational theory allows contextualization of these criteria from a higher-order perspective and leads to seven configurations of actor-structure engagements in terms of engagement for (1) data and technostructure, (2) operational process optimization, (3) operators' employment, (4) employees' wellbeing, (5) proficiency, (6) accountability, and (7) interactive cross-domain design. Each has one criterion of human-centricity in the foreground. Trustworthiness does not build its own configuration but is proposed to be a necessary condition in all seven configurations.

Discussion

The article contextualizes the overall debate on human-centricity and allows us to specify stakeholder-related engagements and how these complement each other. This is of high value for practitioners bringing human-centricity to the workplace and allows them to compare which criteria are considered in transnational declarations, international norms and standards, or company guidelines.

The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review

Implant dentistry has witnessed a transformative shift with the integration of artificial intelligence (AI) technologies. This article explores the role of AI in implant dentistry, emphasizing its impact on diagnostics, treatment planning, and patient outcomes. AI-driven image analysis and deep learning algorithms enhance the precision of implant placement, reducing risks and optimizing aesthetics. Moreover, AI-driven data analytics provide valuable insights into patient-specific treatment strategies, improving overall success rates. As AI continues to evolve, it promises to reshape the landscape of implant dentistry and lead in an era of personalized and efficient oral healthcare.

Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties

Since the beginning of recorded history, the human brain has been one of the most intriguing structures for scientists and engineers. Over the centuries, newer technologies have been developed based on principles that seek to mimic their functioning, but the creation of a machine that can think and behave like a human remains an unattainable fantasy. This idea is now known as "artificial intelligence". Dentistry has begun to experience the effects of artificial intelligence (AI). These include image enhancement for radiology, which improves the visibility of dental structures and facilitates disease diagnosis. AI has also been utilized for the identification of periapical lesions and root anatomy in endodontics, as well as for the diagnosis of periodontitis. This review is intended to provide a comprehensive overview of the use of AI in modern dentistry's numerous specialties. The relevant publications published between March 1987 and July 2023 were identified through an exhaustive search. Studies published in English were selected and included data regarding AI applications among various dental specialties. Dental practice involves more than just disease diagnosis, including correlation with clinical findings and administering treatment to patients. AI cannot replace dentists. However, a comprehensive understanding of AI concepts and techniques will be advantageous in the future. AI models for dental applications are currently being developed.

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

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.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

In the Hands of a Robot, From the Operating Room to the Courtroom: The Medicolegal Considerations of Robotic Surgery

Robotic surgery has rapidly evolved as a groundbreaking field in medicine, revolutionizing surgical practices across various specialties. Despite its numerous benefits, the adoption of robotic surgery faces significant medicolegal challenges. This article delves into the underexplored legal implications of robotic surgery and identifies three distinct medicolegal problems. First, the lack of standardized training and credentialing for robotic surgery poses potential risks to patient safety and surgeon competence. Second, informed consent processes require additional considerations to ensure patients are fully aware of the technology's capabilities and potential risks. Finally, the issue of legal liability becomes complex due to the involvement of multiple stakeholders in the functioning of robotic systems. The article highlights the need for comprehensive guidelines, regulations, and training programs to navigate the medicolegal aspects of robotic surgery effectively, thereby unlocking its full potential for the future..

The Future of Dental Care: The Manipulation of Dental Instruments & Preparation Towards Automated Tooth Cleaning

Dentistry is an essential practice to maintain the health of the oral cavity. Recent advances in digitization and technology for oral examinations have improved the speed and ease of disease diagnosis and dental treatment. Dental robotics has emerged as a new field of dentistry and offers numerous benefits to dental professionals and society. This paper proposes an innovative design of a dental robot setup with a preliminary study on a head model for the preparation of automated dental exploration in MATLAB and discusses further considerations for automation.

Smart Smile: Revolutionizing Dentistry With Artificial Intelligence

Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.

ChatGPT in Dentistry: A Comprehensive Review

Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence chatbot that uses natural language processing that can respond to human input in a conversational manner. ChatGPT has numerous applications in the health care system including dentistry; it is used in diagnoses and for assessing disease risk and scheduling appointments. It also has a role in scientific research. In the dental field, it has provided many benefits such as detecting dental and maxillofacial abnormalities on panoramic radiographs and identifying different dental restorations. Therefore, it helps in decreasing the workload. But even with these benefits, one should take into consideration the risks and limitations of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This comprehensive review represents data collected from 66 relevant articles using PubMed and Google Scholar as databases. This review aims to discuss all relevant published articles on the use of ChatGPT in dentistry.

Artificial Intelligence and Augmented Reality for Guided Implant Surgery Planning: a Proof of Concept

PURPOSE

To present a novel protocol for authentic three-dimensional (3D) planning of dental implants, using artificial intelligence (AI) and augmented reality (AR).

METHODS

The novel protocol consists of (1) 3D data acquisition, with an intraoral scanner (IOS) and cone-beam computed tomography (CBCT); (2) application of AI for CBCT segmentation to obtain standard tessellation language (STL) models and automatic alignment with IOS models; (3) loading of selected STL models within the AR system and surgical planning with holograms; (4) surgical guide design with open-source computer-assisted-design (CAD) software; and (5) surgery on the patient.

RESULTS

This novel protocol is effective and time-efficient when used for planning simple cases of static guided implant surgery in the partially edentulous patient. The clinician can plan the implants in an authentic 3D environment, without using any radiological guided surgery software. The precision of implant placement looks clinically acceptable, with minor deviations.

CONCLUSIONS

AI and AR technologies can be successfully used for planning guided implant surgery for authentic 3D planning that may replace conventional guided surgery software. However, further clinical studies are needed to validate this protocol.

STATEMENT OF CLINICAL RELEVANCE

The combined use of AI and AR may change the perspectives of modern guided implant surgery for authentic 3D planning that may replace conventional guided surgery software.

Informational Needs for Dental-Oriented Electronic Health Records from Dentists' Perspectives

INTRODUCTION

Information technology is vital to support dental care services and is yet to be thoroughly investigated. This study aims to assess the dentists' needs and requirements for health records from dental care providers' perspectives.

METHODS

In-depth interviews were conducted with dentists during clinic practices. This qualitative research method involves exploring the information and functions dentists use to obtain information from EHR. The target population is the dental staff interacting with the patients and accessing the electronic health records in the government and private sectors.

RESULTS

Thirty-five dentists were interviewed directly after the treatment session and asked four pre-defined questions, the dentists' needs were collected, and the met and unmet needs were presented. The interview results revealed 42 needs (15 were met and 27 were unmet), with an average of 1.17 needs per session. The information needs were categorized into foreground and background information needs and reported in nine main themes.

DISCUSSION

The interviews were analyzed, and as a result, nine themes were generated: chief complaints and symptoms, medical and health history, medications, visual representations of the problem, treatment procedures, X-ray services, needs related to advanced features, needs related to insurance coverage, and finally, information needs related to the treatment environment. The required information and functions mentioned by dentists in the study emphasize the need for integrated modules for oral and medical care services. Generally, it is evident that dentists have substantial unmet needs, and the desired EHR should have functions that cover all dentists' needs.

CONCLUSION

The study's findings demonstrate gaps between current and desired EHR to serve dentists' needs. Dentists need better access to patient history and medical information, progress notes, and X-rays to provide visualization tools for problems and patient charts. Moreover, essential needs were related to messaging capability, educational tools, availability of tutorial videos, and accessing external resources. Information needs were described and should be considered when designing EHR to meet all dentists' needs.

Modular Digital and 3D-Printed Dental Models with Applicability in Dental Education

Background and Objectives: The ever more complex modern dental education requires permanent adaptation to expanding medical knowledge and new advancements in digital technologies as well as intensification of interdisciplinary collaboration. Our study presents a newly developed computerized method allowing virtual case simulation on modular digital dental models and 3D-printing of the obtained digital models; additionally, undergraduate dental students' opinion on the advanced method is investigated in this paper. Materials and Methods: Based on the digitalization of didactic dental models, the proposed method generates modular digital dental models that can be easily converted into different types of partial edentulism scenarios, thus allowing the development of a digital library. Three-dimensionally printed simulated dental models can subsequently be manufactured based on the previously obtained digital models. The opinion of a group of undergraduate dental students (n = 205) on the proposed method was assessed via a questionnaire, administered as a Google form, sent via email. Results: The modular digital models allow students to perform repeated virtual simulations of any possible partial edentulism cases, to project 3D virtual treatment plans and to observe the subtle differences between diverse teeth preparations; the resulting 3D-printed models could be used in students' practical training. The proposed method received positive feedback from the undergraduate students. Conclusions: The advanced method is adequate for dental students' training, enabling the gradual design of modular digital dental models with partial edentulism, from simple to complex cases, and the hands-on training on corresponding 3D-printed dental models.

Applications and perspectives of artificial intelligence, machine learning and “dentronics” in dentistry: A literature review

Objective

The aim of this study was to describe artificial intelligence, machine learning, and "Dentronics" applications and perspectives in dentistry.

Materials and Methods

A literature review was carried out to identify the applications of artificial intelligence in the field of dentistry. A specialized search for information was carried out in three databases such as Scopus, PubMed, and Web of Science. Manuscripts published from January 1988 to November 2021 were analyzed. Articles were included without any restriction by language or country.

Results

Scopus, PubMed, and Web of Science were found to have 215, 1023, and 98 registered manuscripts, respectively. Duplicates (191 manuscripts) were eliminated. Finally, 4 letters, 12 editorials, 5 books, 1 erratum, 54 conference papers, 3 conference reviews, and 222 reviews were excluded.

Conclusions

Artificial intelligence has revolutionized prediction, diagnosis, and therapeutic management in modern dentistry. Finally, artificial intelligence is a potential complement to managing future data in this area.

Using artificial intelligence to predict the final color of leucite-reinforced ceramic restorations

OBJECTIVES

The aim of this study was to evaluate the accuracy of machine learning regression models in predicting the final color of leucite-reinforced glass CAD/CAM ceramic veneer restorations based on substrate shade, ceramic shade, thickness and translucency.

METHODS

Leucite-reinforced glass ceramics in four different shades were sectioned in thicknesses of 0.3, 0.5, 0.7, and 1.2 mm. The CIELab coordinates of each specimen were obtained over four different backgrounds (black, white, A1, and A3) interposed with an experimental translucent resin cement using a calibrated spectrophotometer. The color change (CIEDE2000) values, as well as all the CIELab values for each one of the experimental groups, were submitted to 28 different regression models. Each regression model was adjusted according to the weights of each dependent variable to achieve the best-fitting model.

RESULTS

Different substrates, ceramic shades, and thicknesses influenced the L, a, and b of the final restoration. Of all variables, the substrate influenced the final ceramic shade most, followed by the ceramic thickness and the L, a, and b of the ceramic. The decision tree regression model had the lowest mean absolute error and highest accuracy to predict the shade of the ceramic restoration according to the substrate shade, ceramic shade and thickness.

CLINICAL SIGNIFICANCE

The machine learning regression model developed in the study can help clinicians predict the final color of the ceramic veneers made with leucite-reinforced glass CAD/CAM ceramic HT and LT when cemented with translucent cements, based on the color of the substrate and ceramic thicknesses.

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009-2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.

ACCURACY OF INTRAORAL SCANNERS VERSUS TRADITIONAL IMPRESSIONS: A RAPID UMBRELLA REVIEW

PURPOSE

This study aimed to (1) report the trueness and precision of intraoral scanning (IOS) in dentistry based on recent secondary sources and to (2) appraise the reporting quality of the titles and abstracts of the included literature.

MATERIALS AND METHODS

This rapid overview searched the PubMed/Medline and Cochrane Database of Systematic Reviews in March 2021 to identify reviews reporting on the accuracy of IOS. The reference list from the eligible studies was also screened for identification of other potentially eligible studies. The inclusion criteria consisted of English language systematic reviews or meta-analyses published between 2019 and 2021. The exclusion criteria were primary studies, narrative review, and extraoral scanners. The assessment of reporting quality of abstracts of systematic reviews was performed using the reporting checklist PRISMA extension for Abstracts (PRISMA-A). This was a self-funded research project.

RESULTS

Out of the full text screened 25 records, 11 reviews were included. Most studies supported the IOS approach being as precise and accurate as the conventional one. Only one study significantly favored the conventional approach over the IOS, and two studies abstained from making a recommendation. The IOS was significantly superior to the traditional technique in terms of patient preference and time efficiency. After applying PRISMA-A, recommendations for improvements on titles and abstracts of future reviews of IOS and conventional impressions are provided.

CONCLUSION

Laboratory data indicated similar accuracy between IOS and conventional impressions, whereas clinical data found the same in less than 4-unit fixed dental prostheses. For more extensive definitive fixed solutions or removable prostheses, the conventional approach is recommended. IOS was superior in terms of patient preference and time reduction. More clinical trials are required to determine the clinical effectiveness of incorporating IOS in broader scenarios. Better quality of reporting secondary sources abstract is advised.

Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs

OBJECTIVE

The aim of this study was to develop and validate a deep learning-based convolutional neural network (CNN) for the automated detection and categorization of teeth affected by molar-incisor-hypomineralization (MIH) on intraoral photographs.

MATERIALS AND METHODS

The data set consisted of 3241 intraoral images (767 teeth with no MIH/no intervention, 76 with no MIH/atypical restoration, 742 with no MIH/sealant, 815 with demarcated opacity/no intervention, 158 with demarcated opacity/atypical restoration, 181 with demarcated opacity/sealant, 290 with enamel breakdown/no intervention, 169 with enamel breakdown/atypical restoration, and 43 with enamel breakdown/sealant). These images were divided into a training (N = 2596) and a test sample (N = 649). All images were evaluated by an expert group, and each diagnosis served as a reference standard for cyclic training and evaluation of the CNN (ResNeXt-101-32 × 8d). Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve (AUCs) and saliency maps.

RESULTS

The developed CNN was able to categorize teeth with MIH correctly with an overall diagnostic accuracy of 95.2%. The overall SE and SP amounted to 78.6% and 97.3%, respectively, which indicate that the CNN performed better in healthy teeth compared to those with MIH. The AUC values ranging from 0.873 (enamel breakdown/sealant) to 0.994 (atypical restoration/no MIH).

CONCLUSION

It was possible to categorize the majority of clinical photographs automatically by using a trained deep learning-based CNN with an acceptably high diagnostic accuracy.

CLINICAL RELEVANCE

Artificial intelligence-based dental diagnostics may support dental diagnostics in the future regardless of the need to improve accuracy.

Reconstruction of dental roots for implant planning purposes: a feasibility study

Purpose

Modern virtual implant planning is a time-consuming procedure, requiring a careful assessment of prosthetic and anatomical factors within a three-dimensional dataset. In order to facilitate the planning process and provide additional information, this study examines a statistical shape model (SSM) to compute the course of dental roots based on a surface scan.

Material and methods

Plaster models of orthognathic patients were scanned and superimposed with three-dimensional data of a cone-beam computer tomography (CBCT). Based on the open-source software “R”, including the packages Morpho, mesheR, Rvcg and RvtkStatismo, an SSM was generated to estimate the tooth axes. The accuracy of the calculated tooth axes was determined using a leave-one-out cross-validation. The deviation of tooth axis prediction in terms of angle or horizontal shift is described with mean and standard deviation. The planning dataset of an implant surgery patient was additionally analyzed using the SSM.

Results

71 datasets were included in this study. The mean angle between the estimated tooth-axis and the actual tooth-axis was 7.5 ± 4.3° in the upper jaw and 6.7 ± 3.8° in the lower jaw. The horizontal deviation between the tooth axis and estimated axis was 1.3 ± 0.8 mm close to the cementoenamel junction, and 0.7 ± 0.5 mm in the apical third of the root. Results for models with one missing tooth did not differ significantly. In the clinical dataset, the SSM could give a reasonable aid for implant positioning.

Conclusions

With the presented SSM, the approximate course of dental roots can be predicted based on a surface scan. There was no difference in predicting the tooth axis of existent or missing teeth. In clinical context, the estimation of tooth axes of missing teeth could serve as a reference for implant positioning. However, a higher number of training data must be achieved to obtain increasing accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-022-02716-x.

Prediction of Dental Implants Using Machine Learning Algorithms

It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.

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.