Dentronics: Towards robotics and artificial intelligence in dentistry

A Novel Method for Digital Reconstruction of the Mucogingival Borderline in Optical Scans of Dental Plaster Casts

Adequate soft-tissue dimensions have been shown to be crucial for the long-term success of dental implants. To date, there is evidence that placement of dental implants should only be conducted in an area covered with attached gingiva. Modern implant planning software does not visualize soft-tissue dimensions. This study aims to calculate the course of the mucogingival borderline (MG-BL) using statistical shape models (SSM). Visualization of the MG-BL allows the practitioner to consider the soft tissue supply during implant planning. To deploy an SSM of the MG-BL, healthy individuals were examined and the intra-oral anatomy was captured using an intra-oral scanner (IOS). The empirical anatomical data was superimposed and analyzed by principal component analysis. Using a Leave-One-Out Cross Validation (LOOCV), the prediction of the SSM was compared with the original anatomy extracted from IOS. The median error for MG-BL reconstruction was 1.06 mm (0.49–2.15 mm) and 0.81 mm (0.38–1.54 mm) for the maxilla and mandible, respectively. While this method forgoes any technical work or additional patient examination, it represents an effective and digital method for the depiction of soft-tissue dimensions. To achieve clinical applicability, a higher number of datasets has to be implemented in the SSM.

Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs

OBJECTIVES

Intraoral photographs might be considered the machine-readable equivalent of a clinical-based visual examination and can potentially be used to detect and categorize dental restorations. The first objective of this study was to develop a deep learning-based convolutional neural network (CNN) for automated detection and categorization of posterior composite, cement, amalgam, gold and ceramic restorations on clinical photographs. Second, this study aimed to determine the diagnostic accuracy for the developed CNN (test method) compared to that of an expert evaluation (reference standard).

METHODS

The whole image set of 1,761 images (483 of unrestored teeth, 570 of composite restorations, 213 of cements, 278 of amalgam restorations, 125 of gold restorations and 92 of ceramic restorations) was divided into a training set (N=1,407, 401, 447, 66, 231, 93, and 169, respectively) and a test set (N=354, 82, 123, 26, 47, 32, and 44). The expert diagnoses served as a reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve and saliency maps.

RESULTS

After training was complete, the CNN was able to categorize restorations correctly with the following diagnostic accuracy values: 94.9% for unrestored teeth, 92.9% for composites, 98.3% for cements, 99.2% for amalgam restorations, 99.4% for gold restorations and 97.8% for ceramic restorations.

CONCLUSIONS

It was possible to categorize different types of posterior restorations on intraoral photographs automatically with a good diagnostic accuracy.

CLINICAL SIGNIFICANCE

Dental diagnostics might be supported by artificial intelligence-based algorithms in the future. However, further improvements are needed to increase accuracy and practicability.

Recognition and Detection of Wide Field Bionic Compound Eye Target Based on Cloud Service Network

In this paper, a multidisciplinary cross-fusion of bionics, robotics, computer vision, and cloud service networks was used as a research platform to study wide-field bionic compound eye target recognition and detection from multiple perspectives. The current research status of wide-field bionic compound-eye target recognition and detection was analyzed, and improvement directions were proposed. The surface microlens array arrangement was designed, and the spaced surface bionic compound eye design principle cloud service network model was established for the adopted spaced-type circumferential hierarchical microlens array arrangement. In order to realize the target localization of the compound eye system, the content of each step of the localization scheme was discussed in detail. The distribution of virtual spherical targets was designed by using the subdivision of the positive icosahedron to ensure the uniformity of the targets. The spot image was pre-processed to achieve spot segmentation. The energy symmetry-based spot center localization algorithm was explored and its localization effect was verified. A suitable spatial interpolation method was selected to establish the mapping relationship between target angle and spot coordinates. An experimental platform of wide-field bionic compound eye target recognition and detection system was acquired. A super-resolution reconstruction algorithm combining pixel rearrangement and an improved iterative inverse projection method was used for image processing. The model was trained and evaluated in terms of detection accuracy, leakage rate, time overhead, and other evaluation indexes, and the test results showed that the cloud service network-based wide-field bionic compound eye target recognition and detection performs well in terms of detection accuracy and leakage rate. Compared with the traditional algorithm, the correct rate of the algorithm was increased by 21.72%. Through the research of this paper, the wide-field bionic compound eye target recognition and detection and cloud service network were organically provide more technical support for the design of wide-field bionic compound eye target recognition and detection system.

Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study

Background: Artificial intelligence (AI) has taken hold in public health because more and more people are looking to make a diagnosis using technology that allows them to work faster and more accurately, reducing costs and the number of medical errors. Methods: In the present study, 120 panoramic X-rays (OPGs) were randomly selected from the Department of Oral and Maxillofacial Sciences of Sapienza University of Rome, Italy. The OPGs were acquired and analyzed using Apox, which takes a panoramic X-rayand automatically returns the dental formula, the presence of dental implants, prosthetic crowns, fillings and root remnants. A descriptive analysis was performed presenting the categorical variables as absolute and relative frequencies. Results: In total, the number of true positive (TP) values was 2.195 (19.06%); true negative (TN), 8.908 (77.34%); false positive (FP), 132 (1.15%); and false negative (FN), 283 (2.46%). The overall sensitivity was 0.89, while the overall specificity was 0.98. Conclusions: The present study shows the latest achievements in dentistry, analyzing the application and credibility of a new diagnostic method to improve the work of dentists and the patients’ care.

Artificial Intelligence Application in Assessment of Panoramic Radiographs

The aim of this study was to assess the reliability of the artificial intelligence (AI) automatic evaluation of panoramic radiographs (PRs). Thirty PRs, covering at least six teeth with the possibility of assessing the marginal and apical periodontium, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic report of each was generated as the basis of automatic evaluation. The same PRs were manually evaluated by three independent evaluators with 12, 15, and 28 years of experience in dentistry, respectively. The data were collected in such a way as to allow statistical analysis with SPSS Statistics software (IBM, Armonk, NY, USA). A total of 90 reports were created for 30 PRs. The AI protocol showed very high specificity (above 0.9) in all assessments compared to ground truth except from periodontal bone loss. Statistical analysis showed a high interclass correlation coefficient (ICC > 0.75) for all interevaluator assessments, proving the good credibility of the ground truth and the reproducibility of the reports. Unacceptable reliability was obtained for caries assessment (ICC = 0.681) and periapical lesions assessment (ICC = 0.619). The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.

Augmented, Virtual and Mixed Reality in Dentistry: A Narrative Review on the Existing Platforms and Future Challenges

The recent advancements in digital technologies have led to exponential progress in dentistry. This narrative review aims to summarize the applications of Augmented Reality, Virtual Reality and Mixed Reality in dentistry and describes future challenges in digitalization, such as Artificial Intelligence and Robotics. Augmented Reality, Virtual Reality and Mixed Reality represent effective tools in the educational technology, as they can enhance students’ learning and clinical training. Augmented Reality and Virtual Reality and can also be useful aids during clinical practice. Augmented Reality can be used to add digital data to real life clinical data. Clinicians can apply Virtual Reality for a digital wax-up that provides a pre-visualization of the final post treatment result. In addition, both these technologies may also be employed to eradicate dental phobia in patients and further enhance patient’s education. Similarly, they can be used to enhance communication between the dentist, patient, and technician. Artificial Intelligence and Robotics can also improve clinical practice. Artificial Intelligence is currently developed to improve dental diagnosis and provide more precise prognoses of dental diseases, whereas Robotics may be used to assist in daily practice.

Analysis of advances in research trends in robotic and digital dentistry: An original research

Introduction:

The world has been transformed after invention of robotics, digitalization, and artificial intelligence. Their application in the medical field is well recorded; however, their application in dentistry is still being recognized. Hence, in our study, we aimed to analyze the advances in research trends in the digital and the robotics specifically to the dental fields.

Material and Methods:

We conducted a search for articles that recorded the use of robots, digitalization, and artificial intelligence in dentistry, specifically in endodontics. We piloted a questionnaire study to evaluate the awareness and application of these technologies by the clinicians. The results are presented as various applications of these technologies and the number of the articles for various terminologies. The application of these technologies was compared between the clinicians using ANOVA, with P < 0.05 being significant.

Results:

We observed a significant difference between the clinicians regarding the application of these technologies and lower awareness was noted. None of the participants used these technologies in practice. Of the total 20 articles that we had finalized, we observed that these technologies helped in studying the various pathologies and structures that were unviewed previously, as well as treatments, prognosis, and outcomes.

Conclusions:

There is a low awareness of these advanced technologies and application in routine practice. These technologies show greater precision and accuracy. However, the application of these in daily clinical practice and the economy are to be evaluated.

Advanced Applications of Industrial Robotics: New Trends and Possibilities

This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.

Applications of artificial intelligence in dentistry: A comprehensive review

OBJECTIVE

To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research.

MATERIALS AND METHODS

The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years.

RESULTS

Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling.

CONCLUSIONS

The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations.

CLINICAL SIGNIFICANCE

The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

Machine learning for identification of dental implant systems based on shape - A descriptive study

Aim:

To evaluate the efficacy of machine learning in identification of dental implant systems from panoramic radiographs based on the shape.

Settings and Design:

In vitro–Descriptive study

Materials and Methods:

A Dataset of digital panoramic radiographs of three dental implant systems were obtained. The images were divided into two datasets: one for training and another for testing of the machine learning models. Machine learning algorithms namely, support vector machine, logistic regression, K Nearest neighbor and X boost classifiers were trained to classify implant systems from radiographs, based on the shape using Hu and Eigen values. Performance of algorithms was evaluated by its classification accuracy using the test dataset.

Statistical Analysis Used:

Accuracy and recover operating characteristic (ROC) curve were calculated to analyze the performance of the model.

Results:

The classifiers tested in the study were able to identify the implant systems with an average accuracy of 0.67. Of the classifiers trained, logistic regression showed best overall performance followed by SVM, KNN and X boost classifiers.

Conclusions:

Machine learning models tested in the study are proficient enough to identify dental implant systems; hence we are proposing machine learning as a method for implant identification and can be generalized with a larger dataset and more cross sectional studies.

Influence of physical activity on periodontal health in patients with type 2 diabetes mellitus. A blinded, randomized, controlled trial

OBJECTIVES

The aim was to investigate the effect of physical activity on periodontal health and HbA1c levels in patients with type 2 diabetes mellitus (T2DM) over a period of 6 months.

MATERIALS AND METHODS

Thirty-seven patients with non-insulin-dependent T2DM were included in the study. The intervention group (n=20) performed physical activity over a period of 6 months. The control group (n=17) did not receive any intervention. Baseline and final examinations included dental parameters and concentrations of glycosylated hemoglobin (HbA1c) and high-sensitivity C-reactive protein (hsCRP).

RESULTS

Physical activity showed a positive effect on periodontal health. Both the BOP (p= 0.005) and the severity of periodontitis (p= 0.001) were significantly reduced in the intervention group compared to the control group. Furthermore, HbA1c levels were reduced (p= 0.010) significantly in the intervention group while hsCRP levels significantly increased in the control group (p= 0.04).

CONCLUSIONS

Within the limitations of this randomized, controlled trial, physical activity over a period of 6 months is a health-promoting measure for patients with T2DM and improves both periodontal health and HbA1c concentrations.

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

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

Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry

The Scientific Investigation Committee of the American Academy of Restorative Dentistry offers this review of the 2020 professional literature in restorative dentistry to inform busy dentists regarding noteworthy scientific and clinical progress over the past year. Each member of the committee brings discipline-specific expertise to this work to cover this broad topic. Specific subject areas addressed include prosthodontics; periodontics, alveolar bone, and peri-implant tissues; implant dentistry; dental materials and therapeutics; occlusion and temporomandibular disorders (TMDs); sleep-related breathing disorders; oral medicine and oral and maxillofacial surgery; and dental caries and cariology. The authors focused their efforts on reporting information likely to influence day-to-day dental treatment decisions with a keen eye on future trends in the profession. With the tremendous volume of dentistry and related literature being published today, this review cannot possibly be comprehensive. The purpose is to update interested readers and provide important resource material for those interested in pursuing greater detail. It remains our intent to assist colleagues in navigating the extensive volume of important information being published annually. It is our hope that readers find this work useful in successfully managing the dental patients they encounter.

Artificial Intelligence in Biomedical Applications of Zirconia

Artificial intelligence (AI) is rapidly developed based on computer technology, which can perform tasks that customarily require human intelligence by building intelligent software or machines. As a subfield of AI, machine learning (ML) can learn from the intrinsic statistical patterns and structures in data through algorithms to predict invisible data. With the increasing interest in aesthetics in dentistry, zirconia has drawn lots of attention due to its superior biocompatibility, aesthetically pleasing, high corrosion resistance, good mechanical properties, and absence of reported allergic reactions. The evolution of AI and ML led to the development of novel approaches for the biomedical applications of zirconia in dental devices. AI techniques in zirconia-related research and clinical applications have attracted much attention due to their ability to analyze data and reveal correlations between complex phenomena. The AI applications in the field of zirconia science change according to the application direction of zirconia. Therefore, in this article, we focused on AI in biomedical applications of zirconia in dental devices and AI in zirconia-related applications in dentistry.

Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review

OBJECTIVE

The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.

MATERIALS AND METHODS

Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.

RESULTS

The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.

CONCLUSION

The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

Robotic Applications in Orthodontics: Changing the Face of Contemporary Clinical Care

The last decade (2010-2021) has witnessed the evolution of robotic applications in orthodontics. This review scopes and analyzes published orthodontic literature in eight different domains: (1) robotic dental assistants; (2) robotics in diagnosis and simulation of orthodontic problems; (3) robotics in orthodontic patient education, teaching, and training; (4) wire bending and customized appliance robotics; (5) nanorobots/microrobots for acceleration of tooth movement and for remote monitoring; (6) robotics in maxillofacial surgeries and implant placement; (7) automated aligner production robotics; and (8) TMD rehabilitative robotics. A total of 1,150 records were searched, of which 124 potentially relevant articles were retrieved in full. 87 studies met the selection criteria following screening and were included in the scoping review. The review found that studies pertaining to arch wire bending and customized appliance robots, simulative robots for diagnosis, and surgical robots have been important areas of research in the last decade (32%, 22%, and 16%). Rehabilitative robots and nanorobots are quite promising and have been considerably reported in the orthodontic literature (13%, 9%). On the other hand, assistive robots, automated aligner production robots, and patient robots need more scientific data to be gathered in the future (1%, 1%, and 6%). Technological readiness of different robotic applications in orthodontics was further assessed. The presented eight domains of robotic technologies were assigned to an estimated technological readiness level according to the information given in the publications. Wire bending robots, TMD robots, nanorobots, and aligner production robots have reached the highest levels of technological readiness: 9; diagnostic robots and patient robots reached level 7, whereas surgical robots and assistive robots reached lower levels of readiness: 4 and 3, respectively.

Artificial Intelligence in Endodontics: Current Applications and Future Directions

INTRODUCTION

Artificial intelligence (AI) has the potential to replicate human intelligence to perform prediction and complex decision making in health care and has significantly increased its presence and relevance in various tasks and applications in dentistry, especially endodontics. The aim of this review was to discuss the current endodontic applications of AI and potential future directions.

METHODS

Articles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review.

RESULTS

AI models (eg, convolutional neural networks and/or artificial neural networks) have demonstrated various applications in endodontics such as studying root canal system anatomy, detecting periapical lesions and root fractures, determining working length measurements, predicting the viability of dental pulp stem cells, and predicting the success of retreatment procedures. The future of this technology was discussed in light of helping with scheduling, treating patients, drug-drug interactions, diagnosis with prognostic values, and robotic-assisted endodontic surgery.

CONCLUSIONS

AI demonstrated accuracy and precision in terms of detection, determination, and disease prediction in endodontics. AI can contribute to the improvement of diagnosis and treatment that can lead to an increase in the success of endodontic treatment outcomes. However, it is still necessary to further verify the reliability, applicability, and cost-effectiveness of AI models before transferring these models into day-to-day clinical practice.

Dental Robotics: A Disruptive Technology

Robotics is a disruptive technology that will change diagnostics and treatment protocols in dental medicine. Robots can perform repeated workflows for an indefinite length of time while enhancing the overall quality and quantity of patient care. Early robots required a human operator, but robotic systems have advanced significantly over the past decade, and the latest medical robots can perform patient intervention or remote monitoring autonomously. However, little research data on the therapeutic reliability and precision of autonomous robots are available. The present paper reviews the promise and practice of robots in dentistry by evaluating published work on commercial robot systems in dental implantology, oral and maxillofacial surgery, prosthetic and restorative dentistry, endodontics, orthodontics, oral radiology as well as dental education. In conclusion, this review critically addresses the current limitations of dental robotics and anticipates the potential future impact on oral healthcare and the dental profession.

Place of a new radiological index in predicting pulp exposure before intervention for deep carious lesions

BACKGROUND

During interventions for deep caries lesions without severe symptoms, preserving pulpal vitality is important to ensure treatment success, improve organ prognosis, and decrease cost-effectiveness. Current pre-operative radiographs allow visual estimation but not accurate measurement of lesion depth.

PURPOSE

Investigate the ability of ratio 'remaining/total dentin thickness' (RDT/TDT, as determined on pre-operative radiographs) to predict pulp exposure during excavation.

METHODS

This retrospective study (January 2018-June 2020) analyzed data on 360 patients. Four independent raters examined standard pre-operative radiographs and their contrasted versions. Lines put at the dentino-enamel junction, the floor of the carious lesion, and the pulp chamber wall allowed deriving RDT/TDT. Inter-rater agreements and concordance were assessed. A logistic regression accounting for measurement errors provided odds ratios that estimated the ability of the RDT/TDT to predict pulp exposure.

RESULTS

The median RDT/TDT ratio ranges were 16.8-26.5% on standard and 16.2-24.6% on contrasted radiographs. Inter-rater agreements on RDT/TDT were rather poor and inter-rater reliability was low and similar in standard and contrasted radiographs: the concordance correlation coefficients (95% CIs) were estimated at 0.46 (0.40; 0.51) and 0.46 (0.40; 0.52), respectively. The risk of pulp exposure increased by 2.5 times [odds ratio (95% CI) 2.57 (2.06; 3.20)] per 10-point decrease of the ratio on standard radiographs vs. 4.15 (3.15; 5.46) on contrasted radiographs.

CONCLUSION

RDT/TDT ratio is potentially helpful in predicting pulp exposure. However, the measurement errors on RDT and TDT being non-negligible and the interrater agreements poor, there is still place for advances through development of an automated process that will improve reliability and reproducibility of pulp exposure risk assessment.

CLINICAL TRIAL

Trial registration number. ClinicalTrials.gov NCT04607395, October 29, 2020.

The role of AI and machine learning in contemporary orthodontics

In the past 20 years, the orthodontic profession has adopted digital technologies such as computer-assisted tooth movement, automated staged dental aligner production, and 3D simulations. Until recently, the use of artificial intelligence (AI) was limited to narrow intelligence and supervised “learning” such as automated cephalometric point recognition, segmentation of teeth from 3D files, and staging of orthodontic treatment. The next step will be to create neural networks based on general intelligence (the human intelligence is considered general intelligence), where the network using powerful computers and complex algorithms will “learn” orthodontic diagnosis and treatment planning to suggest the most appropriate treatment plan for optimized treatments and more predictable outcomes. The objectives of this paper are to describe the state of the art in AI and orthodontics and explore potential avenues for future developments and applications.

Removable denture is a risk indicator for peri-implantitis and facilitates expansion of specific periodontopathogens: a cross-sectional study

Background

The prevalence of peri-implantitis ranges between 7 and 38.4% depending on risk indicators such as smoking, diabetes mellitus, lack of periodontal maintenance program, and history or presence of periodontitis. Currently, the possible effect of the type of superstructure on peri-implant health is unclear. This cross-sectional study aims to investigate the influence of the superstructure on the prevalence of peri-implant mucositis, peri-implantitis and peri-implant dysbiosis.

Methods

During a 32-month recruitment period dental implants were assessed to diagnose healthy peri-implant tissues, mucositis or peri-implantitis. The study included 1097 implants in 196 patients. Out of all peri-implantitis cases 20 randomly chosen submucosal biofilms from implants with fixed denture (FD) originating from 13 patients and 11 biofilms from implants with removable dentures (RD) originating from 3 patients were studied for microbiome analysis. Composition of transcriptionally active biofilms was revealed by RNAseq. Metatranscriptomic profiles were created for thirty-one peri-implant biofilms suffering from peri-implantitis and microbiome changes associated with superstructure types were identified.

Results

16.41% of the implants were diagnosed with peri-implantitis, 25.00% of implants with RD and 12.68% of implants with FD, respectively. Multivariate analysis showed a significant positive association on patient (p =  < 0.001) and implant level (p = 0.03) between the prevalence of peri-implantitis and RD. Eight bacterial species were associated either with FD or RD by linear discriminant analysis effect size method. However, significant intergroup confounders (e.g. smoking) were present.

Conclusions

Within the limitations of the present work, RDs appear to be a risk indicator for peri-implantitis and seem to facilitate expansion of specific periodontopathogens. Potential ecological and pathological consequences of shift in microbiome from RDs towards higher activity of Fusobacterium nucleatum subspecies animalis and Prevotella intermedia require further investigation.

Validation of motion tracking as tool for observational toothbrushing studies

Video observation (VO) is an established tool for observing toothbrushing behaviour, however, it is a subjective method requiring thorough calibration and training, and the toothbrush position is not always clearly visible. As automated tracking of motions may overcome these disadvantages, the study aimed to compare observational data of habitual toothbrushing as well as of post-instruction toothbrushing obtained from motion tracking (MT) to observational data obtained from VO. One-hundred-three subjects (37.4±14.7 years) were included and brushed their teeth with a manual (MB; n = 51) or a powered toothbrush (PB; n = 52) while being simultaneously video-filmed and tracked. Forty-six subjects were then instructed how to brush their teeth systematically and were filmed/tracked for a second time. Videos were analysed with INTERACT (Mangold, Germany); parameters of interest were toothbrush position, brushing time, changes between areas (events) and the Toothbrushing Systematic Index (TSI). Overall, the median proportion (min; max) of identically classified toothbrush positions (both sextant/surface correct) in a brushing session was 87.8% (50.0; 96.9), which was slightly higher for MB compared to PB (90.3 (50.0; 96.9) vs 86.5 (63.7; 96.5) resp.; p = 0.005). The number of events obtained from MT was higher than from VO (p < 0.001) with a moderate to high correlation between them (MB: ρ = 0.52, p < 0.001; PB: ρ = 0.87; p < 0.001). After instruction, both methods revealed a significant increase of the TSI regardless of the toothbrush type (p < 0.001 each). Motion tracking is a suitable tool for observing toothbrushing behaviour, is able to measure improvements after instruction, and can be used with both manual and powered toothbrushes.

Fracture origin and crack propagation of CAD/CAM composite crowns by combining of in vitro and in silico approaches

PURPOSE

Fractographic analysis has been used to investigate the fracture behavior of Computer-aided design/computer-aided manufacturing (CAD/CAM) composite crowns by subjecting them to compression tests. However, it is difficult to investigate details of the fracture, including its initiation and propagation, using in vitro tests. The aim of this study was to determine the fracture origins and the order of crack initiation of CAD/CAM composite crowns using in silico nonlinear dynamic finite element analysis (FEA).

MATERIAL AND METHODS

The following materials were used: Cerasmart (CS), Katana Avencia Block (KA), and Shofu Block HC (HC) as CAD/CAM crowns, Panavia SA Cement Plus (SA) as a luting material, and Clearfil DC Core Plus (DC) as an abutment. The elastic moduli and fracture strain of each material were obtained from the stress-strain curve of in vitro three-point bending tests. The fracture origins and order of crack initiation of the materials were determined by in silico nonlinear dynamic compression analysis. Load-displacement curves were statistically compared with the results of the in vitro compression tests (Pearson's correlation test, α = 0.05).

RESULTS

The nonlinear dynamic FEA demonstrated that crack initiation was primarily observed near the lingual side of the CAD/CAM crowns and immediately propagated to the central fossa. The models were fractured following the in vitro fracture strains, showing the same order for the products tested (CS/KA/HC, SA, and DC). Load-displacement curves with the use of CS, KA, and HC were significantly correlated to the corresponding in vitro compression tests results (CS: r = 0.985, p < 0.05, KA: r = 0.987, p < 0.05, and HC: r = 0.997, p < 0.05).

CONCLUSIONS

The in silico model established in this study clarified the crack initiation of the CAD/CAM composite crowns and the order of crack initiation among the investigated products, suggesting that the present approach is useful for analyzing the fracture behavior of CAD/CAM composite crowns in detail.

The effect of adjuvant oral irrigation on self-administered oral care in the management of peri-implant mucositis: A randomized controlled clinical trial

OBJECTIVES

This single-blinded randomized clinical trial evaluated the effect of adjuvant oral irrigation in addition to self-administered oral care on prevalence and severity of peri-implant mucositis.

MATERIAL & METHODS

After randomization, patients suffering from peri-implant mucositis were assigned to the following: Group 1 (control) received oral hygiene instruction following a standardized protocol, including a sub- and supramucosal mechanical debridement. Group 2 and 3 additionally were instructed to use an oral irrigator with either water or 0.06% CHX solution. One implant per patient was considered for examination. Clinical examinations included Probing Depth, Bleeding on Probing (BOP-positive sites), and Modified Plaque and Gingival Index. A surrogate variable (mucositis severity score) was applied measuring severity of disease. Statistical analysis included linear regression models and sensitivity analysis.

RESULTS

Sixty periodontally healthy patients were examined for presence and severity of peri-implant mucositis. 70% of all patients reached complete resolution of disease after 12 weeks. The prevalence of peri-implant mucositis after 12 weeks was 50% in group 1, 35% in group 2, and 5% in group 3. Average BOP-positive sites were reduced in all groups after 12 weeks (mean change from baseline: group 1: -1.5; group 2: -1.8; group 3: -2.3).

CONCLUSION

Within the limits of the study, adjuvant use of an oral irrigator with 0.06% CHX in addition to mechanical biofilm removal and oral hygiene instruction can reduce the presence and severity of peri-implant mucositis after 12 weeks.

The effect of keratinized mucosa on the severity of peri-implant mucositis differs between periodontally healthy subjects and the general population: a cross-sectional study

Objective

The study aims to investigate the effect of reduced keratinized mucosa (KM) and other risk indicators on the severity of peri-implant mucositis in (i) the general population, (ii) in periodontally healthy patients, and (iii) in periodontally healthy patients without a history of periodontitis.

Materials and methods

Anamnesis and the following clinical parameters were taken: mucosal-index, bleeding on probing, local plaque index, oral hygiene-index, and width of KM. Mucositis severity score was determined for each implant. Multi-level and subgroup analysis was performed on the patient and implant level.

Results

Six hundred twelve implants in 130 patients were analyzed. Subgroup analysis showed significant associations between KM < 2 mm and the severity score in (ii) periodontally healthy patients (p = 0.014) and in (iii) patients without history of periodontitis (p = 0.017). Secondary outcome showed higher severity scores for patients with insufficient oral hygiene or without residual teeth (p ≤ 0.001), in maxillary implants (p = 0.04), and for the number of implants per patient (p ≤ 0.001).

Conclusion

Within the limits of the study, one may conclude that a reduced width of KM is a risk indicator for the severity of peri-implant mucositis in periodontally healthy patients and patients without a history of periodontitis.

Clinical relevance

The results indicate a band of ≥ 2 mm KM to reduce the severity of peri-implant mucositis in periodontally healthy patients.