Accuracy of Endodontic Access Cavities Performed Using an Augmented Reality Appliance: An In Vitro Study

From Virtual Reality to Reality: Fine-Tuning the Taxonomy for Extended Reality Simulation in Dental Education

INTRODUCTION

Digital simulation in dental education has substantially evolved, addressing several educational challenges in dentistry. Following global lockdowns and sustainability concerns, dental educators are increasingly adopting digital simulation to enhance or replace traditional training methods. This review aimed to contribute to a uniform taxonomy for extended reality (XR) simulation within dental education.

METHODS

This scoping review followed the PRISMA and PRISMA-ScR guidelines. PubMed/MEDLINE, EMBASE, Web of Science and Google Scholar were searched. Eligible studies included English-written publications in indexed journals related to digital simulation in dental/maxillofacial education, providing theoretical descriptions of extended reality (XR) and/or immersive training tools (ITT). The outcomes of the scoping review were used as building blocks for a uniform of XR-simulation taxonomy.

RESULTS

A total of 141 articles from 2004 to 2024 were selected and categorised into Virtual Reality (VR), Mixed Reality (MR), Augmented Reality (AR), Augmented Virtuality (AV) and Computer Simulation (CS). Stereoscopic vision, immersion, interaction, modification and haptic feedback were identified as recurring features across XR-simulation in dentistry. These features formed the basis for a general XR-simulation taxonomy.

DISCUSSION

While XR-simulation features were consistent in the literature, the variety of definitions and classifications complicated the development of a taxonomy framework. VR was frequently used as an umbrella term. To address this, operational definitions were proposed for each category within the virtuality continuum, clarifying distinctions and commonalities.

CONCLUSION

This scoping review highlights the need for a uniform taxonomy in XR simulation within dental education. Establishing a consensus on XR-related terminology and definitions facilitates future research, allowing clear evidence reporting and analysis. The proposed taxonomy may also be of use for medical education, promoting alignment and the creation of a comprehensive body of evidence in XR technologies.

Endodontic access with different computer navigation systems in calcified root canals

BACKGROUND

A range of computer-aided navigation techniques to aid endodontic access cavity preparation have been developed. The aim of this study was to analyze the accuracy of access cavities prepared with the aid of computer-aided static navigation (SN), computer-aided dynamic navigation (DN), and navigation based on augmented reality (AR) compared with a conventional freehand (FH) method in extracted mandibular teeth with calcified root canal systems.

METHODS

Forty single-rooted mandibular teeth were divided into 4 groups, and preoperative cone-beam computed tomographic scans and digital impressions through an intraoral scan were obtained. Access cavities were then prepared using SN (n = 10), DN (n = 10), AR (n = 10), or FH (n = 10), and postoperative cone-beam computed tomographic scans of each tooth were obtained to evaluate deviation of the access cavities between the virtually planned preoperative preparations and the actual postoperative preparations. Analysis of variance followed by Tukey post hoc tests were used to identify significant differences in deviation, with P values below .05 being considered significant.

RESULTS

Significant deviations of the access cavities were found coronally between SN and DN (P < .001), SN and AR (P < .001), DN and FH (P = .015), and AR and FH (P = .003) and apically between SN and AR (P = .003) and AR and FH (P = .006). There were significant differences at angular level between SN and DN (P < .001), SN and AR (P < .001), and SN and FH (P = .013).

CONCLUSIONS

AR was associated with significantly smaller differences in dentin removal and cavity alignment than the SN, DN, and FH methods.

PRACTICAL IMPLICATIONS

Endodontic access cavity preparations using AR technology were more accurate than preparations using other techniques and have the potential to be adopted in clinical practice when canal systems are obliterated.

Augmented Reality in Dentistry: Enhancing Precision in Clinical Procedures-A Systematic Review

Background: Augmented reality (AR) enhances sensory perception by adding extra information, improving anatomical localization and simplifying treatment views. In dentistry, digital planning on bidimensional screens lacks real-time feedback, leading to potential errors. However, it is not clear if AR can improve the clinical treatment precision. The aim of this research is to evaluate if the use of AR-based instruments could improve dental procedure precision. Methods: This review covered studies from January 2018 to June 2023, focusing on AR in dentistry. The PICO question was "Does AR increase the precision of dental interventions compared to non-AR techniques?". The systematic review was carried out on electronic databases, including Ovid MEDLINE, PubMed, and the Web of Science, with the following inclusion criteria: studies comparing the variation in the precision of interventions carried out with AR instruments and non-AR techniques. Results: Thirteen studies were included. Conclusions: The results of this systematic review demonstrate that AR enhances the precision of various dental procedures. The authors advise clinicians to use AR-based tools in order to improve the precision of their therapies.

The Use of Artificial Intelligence in Endodontics

Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning.

Artificial Intelligence in Endodontic Education

AIMS

The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients.

METHODS

An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov.

RESULTS

The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization.

CONCLUSIONS

AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.

Expert consensus on digital guided therapy for endodontic diseases

Digital guided therapy (DGT) has been advocated as a contemporary computer-aided technique for treating endodontic diseases in recent decades. The concept of DGT for endodontic diseases is categorized into static guided endodontics (SGE), necessitating a meticulously designed template, and dynamic guided endodontics (DGE), which utilizes an optical triangulation tracking system. Based on cone-beam computed tomography (CBCT) images superimposed with or without oral scan (OS) data, a virtual template is crafted through software and subsequently translated into a 3-dimensional (3D) printing for SGE, while the system guides the drilling path with a real-time navigation in DGE. DGT was reported to resolve a series of challenging endodontic cases, including teeth with pulp obliteration, teeth with anatomical abnormalities, teeth requiring retreatment, posterior teeth needing endodontic microsurgery, and tooth autotransplantation. Case reports and basic researches all demonstrate that DGT stand as a precise, time-saving, and minimally invasive approach in contrast to conventional freehand method. This expert consensus mainly introduces the case selection, general workflow, evaluation, and impact factor of DGT, which could provide an alternative working strategy in endodontic treatment.

A novel mixed reality-guided dental implant placement navigation system based on virtual-actual registration

BACKGROUNDS

The key to successful dental implant surgery is to place the implants accurately along the pre-operative planned paths. The application of surgical navigation systems can significantly improve the safety and accuracy of implantation. However, the frequent shift of the views of the surgeon between the surgical site and the computer screen causes troubles, which is expected to be solved by the introduction of mixed-reality technology through the wearing of HoloLens devices by enabling the alignment of the virtual three-dimensional (3D) image with the actual surgical site in the same field of view.

METHODS

This study utilized mixed reality technology to enhance dental implant surgery navigation. Our first step was reconstructing a virtual 3D model from pre-operative cone-beam CT (CBCT) images. We then obtained the relative position between objects using the navigation device and HoloLens camera. Via the algorithms of virtual-actual registration, the transformation matrixes between the HoloLens devices and the navigation tracker were acquired through the HoloLens-tracker registration, and the transformation matrixes between the virtual model and the patient phantom through the image-phantom registration. In addition, the algorithm of surgical drill calibration assisted in acquiring transformation matrixes between the surgical drill and the patient phantom. These algorithms allow real-time tracking of the surgical drill's location and orientation relative to the patient phantom under the navigation device. With the aid of the HoloLens 2, virtual 3D images and actual patient phantoms can be aligned accurately, providing surgeons with a clear visualization of the implant path.

RESULTS

Phantom experiments were conducted using 30 patient phantoms, with a total of 102 dental implants inserted. Comparisons between the actual implant paths and the pre-operatively planned implant paths showed that our system achieved a coronal deviation of 1.507 ± 0.155 mm, an apical deviation of 1.542 ± 0.143 mm, and an angular deviation of 3.468 ± 0.339°. The deviation was not significantly different from that of the navigation-guided dental implant placement but better than the freehand dental implant placement.

CONCLUSION

Our proposed system realizes the integration of the pre-operative planned dental implant paths and the patient phantom, which helps surgeons achieve adequate accuracy in traditional dental implant surgery. Furthermore, this system is expected to be applicable to animal and cadaveric experiments in further studies.

Novel method for augmented reality guided endodontics: an in vitro study

OBJECTIVE

The aim of this study is to evaluate the accuracy in endodontics of a novel augmented reality (AR) method for guided access cavity preparation in 3D-printed jaws.

METHODS

Two operators with different levels of experience in endodontics performed pre-planned virtually guided access cavities through a novel markerless AR system developed by a team among the authors on three sets of 3D-printed jaw models using a 3D printer (Objet Connex 350, Stratasys) mounted on a phantom. After the treatment, a post-operative high-resolution CBCT scan (NewTom VGI Evo, Cefla) was taken for each model and registered to the pre-operative model. All the access cavities were then digitally reconstructed by filling the cavity area using 3D medical software (3-Matic 15.0, Materialise). For the anterior teeth and the premolars, the deviation at the coronal and apical entry points as well as the angular deviation of the access cavity were compared to the virtual plan. For the molars, the deviation at the coronal entry point was compared to the virtual plan. Additionally, the surface area of all access cavities at the entry point was measured and compared to the virtual plan. Descriptive statistics for each parameter were performed. A 95% confidence interval was calculated.

RESULTS

A total of 90 access cavities were drilled up to a depth of 4 mm inside the tooth. The mean deviation in the frontal teeth and in the premolars at the entry point was 0.51 mm and 0.77 mm at the apical point, with a mean angular deviation of 8.5° and a mean surface overlap of 57%. The mean deviation for the molars at the entry point was 0.63 mm, with a mean surface overlap of 82%.

CONCLUSION

The use of AR as a digital guide for endodontic access cavity drilling on different teeth showed promising results and might have potential for clinical use. However, further development and research might be needed before in vivo validation to overcome the limitations of the study.