New Frontier in Advanced Dentistry: CBCT, Intraoral Scanner, Sensors, and Artificial Intelligence in Dentistry

Digital intraoral and radiologic records in forensic identification: Match with disruptive technology

While clinical dentistry has seamlessly integrated the digital revolution, there is a gap in the technological capabilities of forensic dentistry.The study aimed to compare the superimposition accuracy of two different three-dimensional record formats, namely the intraoral scanner and cone beam computer tomography, in the context of forensic identification.The sample consisted of randomly selected adults (n=10) of both sexes aged between 20 and 50 years. Following the acquisition of data using the Medit i700 wireless scanner and the iCAT Tomograph with InVivo software, the records were analysed and compared through superimposition using Medit Scan Clinic software to assess the technical precision of anatomical identification details.The results obtained through the superimposition of dental and bone records following intra- and inter-observer analysis enabled an accurate comparison and identification of an individual. This method can differentiate between positive and negative matches, achieving exclusion results and offering a potential solution to overcoming the absence of a standardisation procedure in human identification.

New Approaches and Technologies in Orthodontics

In recent years, new diagnostic and treatment approaches in orthodontics have arisen, and there is thus a need for researchers and practitioners to stay up to date with these innovations [...].

Effectiveness of creating digital twins with different digital dentition models and cone-beam computed tomography

Distortion of dentition may occur in cone-beam computed tomography (CBCT) scans due to artifacts, and further imaging is frequently required to produce digital twins. The use of a plaster model is common; however, it has certain drawbacks. This study aimed to assess the feasibility of different digital dentition models over that of plaster casts. Plaster models, alginate impressions, intraoral scan (IOS) images, and CBCT images of 20 patients were obtained. The desktop model scanner was used to scan the alginate impression twice, five minutes and two hours after impression-making. Using an IOS, the full arch was scanned in segments using CS 3600 and simultaneously with i700 wireless. The digital twins obtained from the alginate impression and IOS were superimposed with those obtained from the plaster cast. The differences and distances at each reference point were measured. Scans of alginate impressions after two hours showed the greatest discrepancies, but these were all less than the CBCT voxel size of 0.39 mm. Alginate impression scans and IOS are suitable supplements to CBCT compared to the plaster model. Accuracy can be improved by scanning the alginate impression within five minutes or by intraoral scanning of the entire arch with segmentation.

Full-Arch Guided Restoration and Bone Regeneration: A Complete Digital Workflow Case Report

OBJECTIVE

complex rehabilitations present multiple difficulties, regarding both the planification of the surgery and the design of the prothesis. A digital approach can support the workflow, as well as the degree of intraoperative precision, and improve the long-term prognosis.

METHODS

A surgical guide was designed for implant placement. An extensive regeneration of the upper jaw was performed with contextual implant insertion, and a delayed load rehabilitation was chosen. After four months, a second surgery and a simultaneous soft tissue augmentation was performed, and a 3D-printed temporary restoration was placed. After another two months, new dental and facial scans, smile design, and facial bite registrations were obtained. Upper and lower dentures were built using an exclusively digital workflow. Both metal substructures were passivated and cemented in one session; in the following appointment, the aesthetic and occlusal checks were carried out. During the third visit, both prostheses were delivered.

RESULTS

Careful case planning and the surgical guide made it possible to achieve primary stability and acceptable emergence profiles in an extremely reabsorbed upper jaw. Leukocyte-Platelet Rich Fibrin (L-PRF) made the extensive bone regeneration more approachable and lowered the post-operative pain and swelling, while speeding up the soft tissue healing process. During the re-entry surgery, the volumes of soft tissues were increased to improve aesthetics, and the amount of keratinized gingiva around the six implants was also increased. Smile design and facial scans have provided the means to create acceptable aesthetics and function in a few sessions with minimal patient discomfort.

CONCLUSIONS

Computer-assisted implantology is a safe and precise method of performing dental implant surgery. Preliminary studies have a high degree of accuracy, but further studies are needed to arrive at a fully digital clinical protocol at all stages.

Artificial Intelligence in Medicine and Dentistry

INTRODUCTION

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

OBJECTIVE

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

CONCLUSION

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis

The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35-0.95) and the specificity was 0.79 (95% CI 0.75-0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient's panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.

Cone-Beam Computed Tomography and the Related Scientific Evidence

Cone-beam computed tomography (CBCT) is the most common three-dimensional (3D) imaging technique used in dentistry [...]

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

3D Cephalometric Normality Range: Auto Contractive Maps (ACM) Analysis in Selected Caucasian Skeletal Class I Age Groups

The objective of this paper is to define normal values of a novel 3D cephalometric analysis and to define the links through an artificial neural network (ANN). Methods: One hundred and fifteen CBCTs of Class I young patients, distributed among gender-adjusted developmental groups, were selected. Three operators identified 18 cephalometric landmarks from which 36 measurements were obtained. The repeatability was assessed through the ICC. Two-dimensional values were extracted by an automatic function, and the mean value and standard deviation were compared by paired Student’s t-tests. Correlation coefficient gave the relationships between 2D and 3D measurements for each group. The values were computed with the ANN to evaluate the parameters normality link and displayed by Pajek software. Results: The ICC assessed an excellent (≥0.9) repeatability. Normal values were extracted, and compared with 2D measurements, they showed a high correlation on the mid-sagittal plane, reaching 1.00, with the lowest 0.71 on the lateral plane. The ANN showed strong links between the values with the centrality of the go-sagittal plane compared to the rest. Conclusions: The study provides a set of 3D cephalometric values obtained by the upper and lower 95% CI for the mean divided into the developmental stage subgroups. The two-dimensional measurements showed variable concordance, while the ANN showed a centrality between the parameters.