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

Augmented Reality Head-Mounted Device and Dynamic Navigation System for Postremoval in Maxillary Molars

INTRODUCTION

This study evaluates the feasibility of an augmented reality (AR) head-mounted device (HMD) displaying a dynamic navigation system (DNS) in the surgical site for fiber postremoval in maxillary molars and compares it to the DNS technique.

METHODS

Fifty maxillary first molars were divided into 2 groups: AR HMD + DNS (n = 25) and DNS (n = 25). The palatal canal was restored with RelyX fiber post (3M ESPE) luted with RelyX Unicem (3M ESPE). A core buildup was performed using Paracore (Coltene/Whaledent). Cone beam computed tomography (CBCT) scans were taken before and after postremoval. The drilling trajectory and depth were planned under X-guide software (X-Nav Technologies, Lansdale, PA). For the AR HMD + DNS group, the AR HMD (Microsoft HoloLens 2) displayed the DNS in the surgical site. The three dimensional (3D) deviations (Global coronal deviation [GCD] and global apical deviation [GAD]) and angular deflection (AD) were calculated. The number of mishaps and operating time were recorded.

RESULTS

Fiber post was removed from all samples (50/50). The AR HMD + DNS was more accurate than DNS, showing significantly lower GCD and GAD deviations and AD (P < .05). No mishap was detected. The AR HMD + DNS was as efficient in time as DNS (P > .05).

CONCLUSIONS

Within the limitations of this in vitro study, the AR HMD can safely display DNS in the surgical site for fiber post-removal in maxillary molars. AR HMD improved the DNS accuracy. Both AR HMD + DNS and DNS were time-efficient for fiber postremoval in maxillary molars.

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.

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

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

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.

Augmented Reality and 3-Dimensional Dynamic Navigation System Integration for Osteotomy and Root-end Resection

INTRODUCTION

Augmented reality (AR) superimposes high-definition computer-generated virtual content onto the existing environment, providing users with an enhanced perception of reality. This study investigates the feasibility of integrating an AR head-mounted device into a 3-dimensional dynamic navigation system (3D-DNS) for osteotomy and root-end resection (RER). It compares the accuracy and efficiency of AR + 3D-DNS to 3D-DNS for osteotomy and RER.

METHODS

Seventy-two tooth roots of 3D-printed surgical jaw models were divided into two groups: AR + 3D-DNS (n = 36) and 3D-DNS (n = 36). Cone-beam computed tomography scans were taken pre and postoperatively. The osteotomy and RER were virtually planned on X-guide software and delivered under 3D-DNS guidance. For the AR + 3D-DNS group, an AR head-mounted device (Microsoft HoloLens 2) was integrated into the 3D-DNS. The 2D- and 3D-deviations were calculated. The osteotomy and RER time and the number of procedural mishaps were recorded.

RESULTS

Osteotomy and RER were completed in all samples (72/72). AR + 3D-DNS was more accurate than 3D-DNS, showing lower 2D- and 3D-deviation values (P < .05). The AR + 3D-DNS was more efficient in time than 3D-DNS (P < .05). There was no significant difference in the number of mishaps (P > .05).

CONCLUSIONS

Within the limitations of this in vitro study, the integration of an AR head-mounted device to 3D-DNS is feasible for osteotomy and RER. AR improved the accuracy and time efficiency of 3D-DNS in osteotomy and RER. Head-mounted AR has the potential to be safely and reliably integrated into 3D-DNS for endodontic microsurgery.

Changes in alveolar bone width around maxillary implants, as determined through cone beam computed tomography based on bony landmarks: A preliminary study

PURPOSE

This study aimed to investigate changes in alveolar bone width around dental implants and identify the anterior nasal spine (ANS), posterior nasal spine (PNS), and floor of the nasal cavity that can be used as reference landmarks for standardizing the orientation of different cone-beam computed tomography (CBCT) scans.

MATERIALS AND METHODS

We enrolled two groups that comprised 30 implants. Two CBCT scans from the same patient after implant surgery in the first group were obtained to determine differences in the relative distance and angle between the ANS and apex of the dental implant. Then we compared the second group of patients' presurgical and postsurgical CBCT images to evaluate changes in alveolar bone width after dental implant surgery by the aforementioned bony landmarks.

RESULTS

In the first group, no statistically significant differences were detected in the mean distance between the ANS, PNS and implant tip in different directions. In the second group, bone width increased at 1 mm (p = 0.020) and decreased at 4 mm (p < 0.001) and 7 mm (p < 0.001) below the alveolar bone crest after implant surgery.

CONCLUSIONS

Within the limitations of the present study, the ANS, PNS, and floor of the nasal cavity can be useful in standardizing the orientation of CBCT scans and alveolar bone remodeling after implant surgery varied depending on the height and direction from the alveolar bone crest based on the three landmarks.