Radiographic diagnosis of periodontal diseases - Current evidence versus innovations.

Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.

Individual "alveolar phenotype" limits dimensions of lateral bone augmentation.

AIM: Alveolar ridge resorption following tooth extraction often renders a lateral bone augmentation inevitable. Some patients, however, suffer from severe early (during graft healing, Eres ) and/or late (during follow-up, Lres ) graft resorption. We explored the hypothesis that the "individual phenotypic dimensions" may partially explain the degree of such resorptions. MATERIALS AND METHODS: Patients who underwent a guided bone regeneration (GBR) procedure were screened for inclusion according to the following criteria: (1) a relatively symmetrical maxillary arch; (2) an intact contra-lateral alveolar bone dimension; (3) the availability of a pre-operative cone-beam CT (CBCT); (4) a CBCT taken immediately after GBR, and (5) at least one CBCT scan ≥6 months after surgery. CBCT scans from different timepoints were registered and imported into the Mimics software (Materialise, Leuven, Belgium). Bone dimensions of the contra-lateral site of the augmentation, representing the "individual phenotypical dimension (IPD) of the alveolar crest", were superimposed on the augmented site and registered accordingly. As such, Eres and Lres could be measured over time, in relation to the IPD (in two dimensions; per millimetre apically from the alveolar crest, in the centre of the GBR), as well as in three dimensions (the entire GBR, 2 mm away from the mesial, distal, and apical border for standardization). RESULTS: A total of 17 patients (23 augmented sites) were included. After Eres , the outline of the augmentation was in general located ±1 mm outside the IPD, but ≥1.5 years after GBR, it further moved towards the IPD (85% within 0.5 mm distance). CONCLUSIONS: Within the limitations of this study, the results indicate that the dimensions of a lateral bone augmentation are defined by the "individual phenotypic bone boundaries" of the patient.

Non-invasive oral implant position assessment: An ex vivo study using a 3D industrial scan as the reference model to mimic the clinical situation.

AIM: To introduce an objective method to evaluate the accuracy of implant position assessment in partially edentulous patients by comparing different techniques (conventional impression, intraoral scan, CBCT) to a reference 3D model obtained with an industrial scanner, the latter mimicking the clinical situation. MATERIALS AND METHODS: Twenty-nine implants were placed in four human cadaver heads using a fully guided flapless protocol. Implant position was assessed using (a) a conventional impression, (b) an intraoral scan, and (c) CBCT and compared to an industrial scan. Three-dimensional models of intraoral scan body and implant were registered to the arch models and the deviation at implant shoulder, apex, and the angle of deviation were compared to each other as well as to the reference model. RESULTS: The three assessment techniques showed statistically significant deviations (p < .01) from the industrial scan, for all measurements, with no difference between the techniques. The maximum deviation at the implant shoulder was 0.16 mm. At the implant apex this increased to 0.38 mm. The intraoral scan deviated significantly more than the CBCT (0.12 mm, p < .01) and the conventional impression (0.10 mm, p = .02). The maximum implant angle deviation was 1.0°. The intraoral scan deviated more than the conventional impression (0.3°, p = .02). CONCLUSION: All assessment techniques deviated from the reference industrial scan, but the differences were relatively small. Intraoral scans were slightly less accurate than both conventional impressions and CBCT. Depending on the application, however, this inaccuracy may not be clinically relevant.

The impact of implant-related characteristics on dental implant blooming: An in vitro study.

AIM: To assess, in vitro, variables potentially influencing implant blooming using a human-like imaging phantom and 3D-printed mandibles. MATERIAL AND METHODS: Sixty implants were inserted in 3D-printed mandibles in 26 different configurations in order to examine the impact of implant diameter, presence of a cover screw, implant design/material, implant position, and the presence of additional implants on implant blooming using two cone-beam computed tomography (CBCT) devices (Accuitomo [ACC] and NewTom [NWT]). Two observers measured the amount of implant blooming in both buccolingual and mesiodistal directions. Inter-rater agreement and descriptive statistics, grouped by implant characteristic and CBCT device, were calculated. RESULTS: Both CBCT devices increased implant diameter (a mean increase of 9.2% and 11.8% for titanium, 20.3% and 24.4% for zirconium, for ACC and NWT, respectively). An increase in implant diameter did not increase the amount of blooming, whereas placing a cover screw did (from 8.0% to 10.9% for ACC, and from 10.0% to 15.6% for NWT). Moreover, implant design, anatomical region, and the presence of another implant also affected the extent of the blooming. CONCLUSIONS: Dental implants show a clear diameter increase on CBCT, with the effect being more pronounced for zirconium than for titanium implants. Similar effects are likely to occur in the clinical setting, potentially masking nonosseointegration, reducing the dimensions of peri-implant defects, and/or causing underestimation of the buccal bone thickness.

Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice.

The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs.

OBJECTIVE: To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs. MATERIALS AND METHODS: In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations. RESULTS: The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual. CONCLUSIONS: The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone. CLINICAL SIGNIFICANCE: An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.

Standard Drilling Versus Ultrasonic Implant Site Preparation: A Clinical Study at 4 Weeks After Insertion of Conical Implants.

OBJECTIVE: Clinically evaluate implants placed after ultrasonic implant site preparation (UISP) and standard drilling (SD). MATERIALS AND METHODS: Ten patients received 21 implants placed using UISP (n = 11) or SD (n = 10). Bone quality was hand assessed and final insertion torque (IT), resonance frequency analysis (ISQ) at baseline and ISQ, and removal torque values (RTV) at 4 weeks were recorded and compared. RESULTS: Mean IT values were 70.91 and 72.40 N/cm in UISP and SD groups, respectively, and were not statistically different. IT significantly correlated to bone quality. Mean ISQ values at baseline and 4 weeks were not significantly different and were 74.72 and 74.73 for UISP and 76.70 and 73.20 for SD, respectively. Mean ISQ at baseline significantly correlated to IT values and bone quality in both groups. Mean RTV values in both UISP (51.32 N/cm) and SD (53.1 N/cm) were not significantly different but significantly correlated to IT values. All implants achieved osseointegration and were restored. CONCLUSION: Implant placement after ultrasonic preparation can be considered a predictable technique leading to clinical and biological responses similar to SD 4 weeks after insertion.

The Socket Shield Technique: Stability of the Buccal Peri-implant Bone after Partial Root Removal - A Prospective Case Series of 20 patients, with 18 Months Follow-up.

OBJECTIVE: This study aims to collect data on implant survival, bone volume maintenance, and complications associated with the socket shield technique. BACKGROUND DATA: The socket shield technique was introduced in 2010. Since then, several systematic reviews have been published, showing good clinical outcomes. The behaviour of the buccal bone plate is so far not completely understood. METHODS: The study involved the placement of 23 implants using the socket shield technique in 20 patients. AstraTech EV implants were used, and no bone substitutes or connective tissue grafts were applied. Patients were monitored for 18 months, recording implant survival, volumetric bone analysis on CBCT scans, interproximal bone levels, bone sounding, pink esthetic scores, and complications. Prosthetic procedures were also described, including temporary and final restorations. RESULTS: A 95.7% cumulative 18-month implant survival rate was obtained using the socket shield technique, with a significant but limited reduction in buccal bone thickness (BBT) after implant placement. One implant did not integrate and two shields were partially exposed. The mean pink esthetic score, 1 year after loading was 12.93 ± 1.22. CONCLUSION: The study suggests that the socket shield technique can result in limited reduction of the buccal bone volume, with a high implant survival rate. Re-entry studies are recommended to investigate the causes of bone resorption.

Bone-to-Implant Contact in Implants with Plasma-Treated Nanostructured Calcium-Incorporated Surface (XPEEDActive) Compared to Non-Plasma-Treated Implants (XPEED): A Human Histologic Study at 4 Weeks.

Titanium implants undergo an aging process through surface hydrocarbon deposition, resulting in decreased wettability and bioactivity. Plasma treatment was shown to significantly reduce surface hydrocarbons, thus improving implant hydrophilicity and enhancing the osseointegration process. This study investigates the effect of plasma surface treatment on bone-to-implant contact (BIC) of implants presenting a nanostructured calcium-incorporated surface (XPEED®). Following a Randomized Controlled Trial (RCT) design, patients undergoing implant surgery in the posterior maxilla received additional plasma-treated (n = 7) or -untreated (n = 5) 3.5 × 8 mm implants that were retrieved after a 4-week healing period for histological examination. Histomorphometric analysis showed that plasma-treated implants exhibited a 38.7% BIC rate compared to 22.4% of untreated implants (p = 0.002), indicating enhanced osseointegration potential. Histological images also revealed increased bone formation and active osteoblastic activity around plasma-treated implants when compared to untreated specimens. The findings suggest that plasma treatment improves surface hydrophilicity and biological response, facilitating early bone formation around titanium implants. This study underscores the importance of surface modifications in optimizing implant integration and supports the use of plasma treatment to enhance osseointegration, thereby improving clinical outcomes in implant dentistry and offering benefits for immediate and early loading protocols, particularly in soft bone conditions.

Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation.

INTRODUCTION: As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes. METHODS: Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios. RESULTS: The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04). CONCLUSIONS: By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.

Nanostructured Calcium-Incorporated Surface Compared to Machined and SLA Dental Implants-A Split-Mouth Randomized Case/Double-Control Histological Human Study.

Background: Implant surface topography is a key element in achieving osseointegration. Nanostructured surfaces have shown promising results in accelerating and improving bone healing around dental implants. The main objective of the present clinical and histological study is to compare, at 4 and 6 weeks, (w) bone-to-implant contact in implants having either machined surface (MAC), sandblasted, large grit, acid-etched implant surface (SLA) medium roughness surface or a nanostructured calcium-incorporated surface (XPEED®). Methods: 35 mini-implants of 3.5 × 8.5 mm with three different surface treatments (XPEED® (n = 16)­SLA (n = 13)­MAC (n = 6), were placed in the posterior maxilla of 11 patients (6 females and 5 males) then, retrieved at either 4 or 6w in a randomized split-mouth study design. Results: The BIC rates measured at 4w and 6w respectively, were: 16.8% (±5.0) and 29.0% (±3.1) for MAC surface; 18.5% (±2.3) and 33.7% (±3.3) for SLA surface; 22.4% (±1.3) and 38.6% (±3.2) for XPEED® surface. In all types of investigated surfaces, the time factor appeared to significantly increase the bone to implant contact (BIC) rate (p < 0.05). XPEED® surface showed significantly higher BIC values when compared to both SLA and MAC values at 4w (p < 0.05). Also, at 6w, both roughened surfaces (SLA and XPEED®) showed significantly higher values (p < 0.05) than turned surface (MAC). Conclusions: Nanostructured Calcium titanate coating is able to enhance bone deposition around implants at early healing stages.

Automated segmentation of the mandibular canal and its anterior loop by deep learning.

Accurate mandibular canal (MC) detection is crucial to avoid nerve injury during surgical procedures. Moreover, the anatomic complexity of the interforaminal region requires a precise delineation of anatomical variations such as the anterior loop (AL). Therefore, CBCT-based presurgical planning is recommended, even though anatomical variations and lack of MC cortication make canal delineation challenging. To overcome these limitations, artificial intelligence (AI) may aid presurgical MC delineation. In the present study, we aim to train and validate an AI-driven tool capable of performing accurate segmentation of the MC even in the presence of anatomical variation such as AL. Results achieved high accuracy metrics, with 0.997 of global accuracy for both MC with and without AL. The anterior and middle sections of the MC, where most surgical interventions are performed, presented the most accurate segmentation compared to the posterior section. The AI-driven tool provided accurate segmentation of the mandibular canal, even in the presence of anatomical variation such as an anterior loop. Thus, the presently validated dedicated AI tool may aid clinicians in automating the segmentation of neurovascular canals and their anatomical variations. It may significantly contribute to presurgical planning for dental implant placement, especially in the interforaminal region.

Diffuse Sclerosing Osteomyelitis: A Potential Risk Indicator for Peri-implantitis? A Case Series.

PURPOSE: To propose diffuse osteomyelitis as risk indicator for peri-implantitis following the loss of several dental implants in patients that present with highly sclerotic bone areas. MATERIALS AND METHODS: A total of six "nightmare cases"-three of which were treated at the Department of Periodontology of the University Hospitals of the Catholic University Leuven and three of which were referred there for a second opinion-were retrospectively analyzed using radiographs obtained via contact with referring clinicians in order to fully reconstruct the treatment pathway and dental history for each of these patients. RESULTS: All patients suffered from early implant failures and/or severe peri-implantitis with bone loss and crater formation up to the apical level, as well as the loss of all or nearly all implants. Re-examination of their preand postoperative CBCTs, in combination with several bone biopsies, confirmed the diagnosis of a diffuse sclerosing osteomyelitis in the treated area. Osteomyelitis could be linked to a longstanding history of chronic and/or therapyresistant periodontal/endodontic pathology. CONCLUSION: The current retrospective case series seems to suggest that diffuse osteomyelitis should be considered as a risk indicator for severe peri-implantitis. Int J Oral Maxillofac Implants 2023;38:503-515. doi: 10.11607/jomi.9773.

Performance of novice versus experienced surgeons for dental implant placement with freehand, static guided and dynamic navigation approaches.

Lack of evidence exists related to the investigation of the accuracy and efficacy of novice versus experienced practitioners for dental implant placement. Hence, the following in vitro study was conducted to assess the accuracy of implant positioning and self-efficacy of novice compared to experienced surgeons for placing implant using freehand (FH), pilot drill-based partial guidance (PPG) and dynamic navigation (DN) approaches. The findings revealed that DN significantly improved the angular accuracy of implant placement compared with FH (P < 0.001) and PPG approaches (P < 0.001). The time required with DN was significantly longer than FH and PPG (P < 0.001), however, it was similar for both novice and experienced practitioners. The surgeon's self-confidence questionnaire suggested that novice practitioners scored higher with both guided approaches, whereas experienced practitioners achieved higher scoring with PPG and FH compared to DN. In conclusion, implant placement executed under the guidance of DN showed high accuracy irrespective of the practitioner's experience. The application of DN could be regarded as a beneficial tool for novices who offered high confidence of using the navigation system with the same level of accuracy and surgical time as that of experienced practitioners.

Real-time simulation of the transplanted tooth using model order reduction.

The biomechanics of transplanted teeth remain poorly understood due to a lack of models. In this context, finite element (FE) analysis has been used to evaluate the influence of occlusal morphology and root form on the biomechanical behavior of the transplanted tooth, but the construction of a FE model is extremely time-consuming. Model order reduction (MOR) techniques have been used in the medical field to reduce computing time, and the present study aimed to develop a reduced model of a transplanted tooth using the higher-order proper generalized decomposition method. The FE model of a previous study was used to learn von Mises root stress, and axial and lateral forces were used to simulate different occlusions between 75 and 175N. The error of the reduced model varied between 0.1% and 5.9% according to the subdomain, and was the highest for the highest lateral forces. The time for the FE simulation varied between 2.3 and 7.2 h. In comparison, the reduced model was built in 17s and interpolation of new results took approximately 2.10-2s. The use of MOR reduced the time for delivering the root stresses by a mean 5.9 h. The biomechanical behavior of a transplanted tooth simulated by FE models was accurately captured with a significant decrease of computing time. Future studies could include using jaw tracking devices for clinical use and the development of more realistic real-time simulations of tooth autotransplantation surgery.

Ethical considerations on artificial intelligence in dentistry: A framework and checklist.

OBJECTIVE: Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS: Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS: The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS: Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE: While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.

Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks.

OBJECTIVE: To assess the accuracy of a novel Artificial Intelligence (AI)-driven tool for automated detection of teeth and small edentulous regions on Cone-Beam Computed Tomography (CBCT) images. MATERIALS AND METHODS: After AI training and testing with 175 CBCT scans (130 for training and 40 for testing), validation was performed on a total of 46 CBCT scans selected for this purpose. Scans were split into fully dentate and partially dentate patients (small edentulous regions). The AI Driven tool (Virtual Patient Creator, Relu BV, Leuven, Belgium) automatically detected, segmented and labelled teeth and edentulous regions. Human performance served as clinical reference. Accuracy and speed of the AI-driven tool to detect and label teeth and edentulous regions in partially edentulous jaws were assessed. Automatic tooth segmentation was compared to manually refined segmentation and accuracy by means of Intersetion over Union (IoU) and 95% Hausdorff Distance served as a secondary outcome. RESULTS: The AI-driven tool achieved a general accuracy of 99.7% and 99% for detection and labelling of teeth and missing teeth for both fully dentate and partially dentate patients, respectively. Automated detections took a median time of 1.5s, while the human operator median time was 98s (P<0.0001). Segmentation accuracy measured by Intersection over Union was 0.96 and 0.97 for fully dentate and partially edentulous jaws respectively. CONCLUSIONS: The AI-driven tool was accurate and fast for CBCT-based detection, segmentation and labelling of teeth and missing teeth in partial edentulism. CLINICAL SIGNIFICANCE: The use of AI may represent a promising time-saving tool serving radiological reporting, with a major step forward towards automated dental charting, as well as surgical and treatment planning.

Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT.

OBJECTIVES: The objective of this study is the development and validation of a novel artificial intelligence driven tool for fast and accurate mandibular canal segmentation on cone beam computed tomography (CBCT). METHODS: A total of 235 CBCT scans from dentate subjects needing oral surgery were used in this study, allowing for development, training and validation of a deep learning algorithm for automated mandibular canal (MC) segmentation on CBCT. Shape, diameter and direction of the MC were adjusted on all CBCT slices using a voxel-wise approach. Validation was then performed on a random set of 30 CBCTs - previously unseen by the algorithm - where voxel-level annotations allowed for assessment of all MC segmentations. RESULTS: Primary results show successful implementation of the AI algorithm for segmentation of the MC with a mean IoU of 0.636 (± 0.081), a median IoU of 0.639 (± 0.081), a mean Dice Similarity Coefficient of 0.774 (± 0.062). Precision, recall and accuracy had mean values of 0.782 (± 0.121), 0.792 (± 0.108) and 0.99 (± 7.64×10-05) respectively. The total time for automated AI segmentation was 21.26 s (±2.79), which is 107 times faster than accurate manual segmentation. CONCLUSIONS: This study demonstrates a novel, fast and accurate AI-driven module for MC segmentation on CBCT. CLINICAL SIGNIFICANCE: Given the importance of adequate pre-operative mandibular canal assessment, Artificial Intelligence could help relieve practitioners from the delicate and time-consuming task of manually tracing and segmenting this structure, helping prevent per- and post-operative neurovascular complications.

Influence of experience on dental implant placement: an in vitro comparison of freehand, static guided and dynamic navigation approaches.

PURPOSE: This study aimed to investigate the performance of novice versus experienced practitioners for placing dental implant using freehand, static guided and dynamic navigation approaches. METHODS: A total of 72 implants were placed in 36 simulation models. Three experienced and three novice practitioners were recruited for performing the osteotomy and implant insertion with freehand, surgical guide (pilot-drill guidance) and navigation (X-Guide, X-Nav technologies) approaches. Each practitioner inserted 4 implants per approach randomly with a 1-week gap to avoid memory bias (4 insertion sites × 3 approaches × 6 practitioners = 72 implants). The performance of practitioners was assessed by comparing actual implant deviation to the planned position, time required for implant placement and questionnaire-based self-confidence evaluation of practitioners on a scale of 1-30. RESULTS: The navigation approach significantly improved angular deviation compared with freehand (P < 0.001) and surgical guide (P < 0.001) irrespective of the experience. Surgical time with navigation was significantly longer compared to the freehand approach (P < 0.001), where experienced practitioners performed significantly faster compared to novice practitioners (P < 0.001). Overall, self-confidence was higher in favor of novice practitioners with both guided approaches. In addition, the confidence of novice practitioners (median score = 26) was comparable to that of experienced practitioners (median score = 27) for placing implants with the navigation approach. CONCLUSIONS: Dynamic navigation system could act as a viable tool for dental implant placement. Unlike freehand and static-guided approaches, novice practitioners showed comparable accuracy and self-confidence to that of experienced practitioners with the navigation approach.

Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography.

INTRODUCTION: Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achieved by merely relying on CBCT intensity variations. This study aimed to develop and validate an artificial intelligence (AI)-driven tool for automated tooth segmentation on CBCT imaging. METHODS: A total of 433 Digital Imaging and Communications in Medicine images of single- and double-rooted teeth randomly selected from 314 anonymized CBCT scans were imported and manually segmented. An AI-driven tooth segmentation algorithm based on a feature pyramid network was developed to automatically detect and segment teeth, replacing manual user contour placement. The AI-driven tool was evaluated based on volume comparison, intersection over union, the Dice score coefficient, morphologic surface deviation, and total segmentation time. RESULTS: Overall, AI-driven and clinical reference segmentations resulted in very similar segmentation volumes. The mean intersection over union for full-tooth segmentation was 0.87 (±0.03) and 0.88 (±0.03) for semiautomated (SA) (clinical reference) versus fully automated AI-driven (F-AI) and refined AI-driven (R-AI) tooth segmentation, respectively. R-AI and F-AI segmentation showed an average median surface deviation from SA segmentation of 9.96 µm (±59.33 µm) and 7.85 µm (±69.55 µm), respectively. SA segmentations of single- and double-rooted teeth had a mean total time of 6.6 minutes (±76.15 seconds), F-AI segmentation of 0.5 minutes (±8.64 seconds, 12 times faster), and R-AI segmentation of 1.2 minutes (±33.02 seconds, 6 times faster). CONCLUSIONS: This study showed a unique fast and accurate approach for AI-driven automated tooth segmentation on CBCT imaging. These results may open doors for AI-driven applications in surgical and treatment planning in oral health care.