ABSTRACTObjectiveTo evaluate the impact of implant surface and instrumentation time on the efficacy of two air powder water‐jet (APWJ) devices in cleaning the implant surface in a simulation of non‐surgical peri‐implantitis treatment.Materials and MethodsTurned and modified surface implants (28 each) were coated with a biofilm imitation and mounted on resin models replicating peri‐implant intra‐osseous defects, including a soft tissue replica. The entire implant periphery was instrumented for 5 or 15s per implant sextant (i.e., in total 30 or 90s per implant), with one of two different APWJ devices using either a glycine or an erythritol powder. Residual biofilm imitation was automatically assessed on standardized photographs and expressed as percentage of the exposed implant surface.ResultsImplant surface (ε2:0.253, p < 0.001) and instrumentation time (ε2:0.044, p = 0.036) had a moderate and small effect, respectively, on the outcome, that is, instrumenting turned compared to modified surface implants as well as using a longer compared to a shorter instrumentation time resulted in less residual biofilm imitation. Complete biofilm imitation removal was achieved only in four turned implants, treated for 15s per sextant. Every second turned implant presented with a maximum of 5% residual biofilm imitation, while only two modified implants achieved this level of cleanliness.ConclusionIn a non‐surgical peri‐implantitis treatment simulation with APWJ devices, superior biofilm imitation removal was achieved at turned implants, and a longer instrumentation time resulted in less residual biofilm imitation. Modified implants had high chances of incomplete biofilm imitation removal, especially at the apical part of the defect. Complete biofilm imitation removal was in general largely unpredictable.

ABSTRACTObjectiveIdentification of the mandibular incisive canal (MIC) prior to anterior implant placement is often challenging. The present study aimed to validate an enhanced artificial intelligence (AI)‐driven model dedicated to automated segmentation of MIC on cone beam computed tomography (CBCT) scans and to compare its accuracy and time efficiency with simultaneous segmentation of both mandibular canal (MC) and MIC by either human experts or a previously trained AI model.Materials and MethodsAn enhanced AI model was developed based on 100 CBCT scans using expert‐optimized MIC segmentation within the Virtual Patient Creator platform. The performance of the enhanced AI model was tested against human experts and a previously trained AI model using another 40 CBCT scans. Performance metrics included intersection over union (IoU), dice similarity coefficient (DSC), recall, precision, accuracy, and root mean square error (RSME). Time efficiency was also evaluated.ResultsThe enhanced AI model had IoU of 93%, DSC of 93%, recall of 94%, precision of 93%, accuracy of 99%, and RMSE of 0.23 mm. These values were significantly higher than those of the previously trained AI model for all metrics, and for manual segmentation for IoU, DSC, recall, and accuracy (p < 0.0001). The enhanced AI model demonstrated significant time efficiency, completing segmentation in 17.6 s (125 times faster than manual segmentation) (p < 0.0001).ConclusionThe enhanced AI model proved to allow a unique and accurate automated MIC segmentation with high accuracy and time efficiency. Besides, its performance was superior to human expert segmentation and a previously trained AI model segmentation.

To investigate the clinically acceptable deviation threshold and the factors associated with trueness across three computer-assisted implant surgery (CAIS) approaches: static CAIS (s-CAIS), dynamic CAIS (d-CAIS), and robotic CAIS (r-CAIS). A total of 314 implants in 194 patients were retrospectively included, with preoperative and postoperative CBCT scans collected. Outcome measures included platform, apex, and angular deviations, which were assessed by registering postoperative CBCT scans to preoperative implant plans using a jawbone matching technique. Generalized linear mixed models (GLMM) were employed to identify significant factors associated with implant trueness. In r-CAIS, mean global deviations were 0.65 ± 0.137 mm (platform), 0.73 ± 0.39 mm (apex), and 1.66° ± 0.99° (angular), significantly smaller than s-CAIS and d-CAIS (p < 0.01). Deviations in d-CAIS were 1.15 ± 0.55 mm, 1.23 ± 0.56 mm, and 2.87° ± 1.71°, whereas s-CAIS showed 0.96 ± 0.56 mm, 1.32 ± 0.67 mm, and 3.42° ± 1.85°. No significant differences were found between s-CAIS and d-CAIS (p > 0.01). All approaches met clinical acceptability thresholds of 1.5 mm for linear and 5° for angular deviation (p < 0.01). Analysis revealed that bone inclinations exceeding 45° significantly increased implant deviations at apex and angular (p < 0.01). Additionally, deviations were greater in the molar region, particularly at the second molar (p < 0.01). All three CAIS approaches demonstrated clinically acceptable trueness, with the r-CAIS showing superior performance. Bone inclination and molar placement were identified as key factors contributing to increased deviations. Chinese Clinical Trial Registry: ChiCTR2400083777.

To compare autogenous bone (AB) harvested from the mandibular ramus or a mixture of bovine bone mineral(BBM) with 25% locally harvested autogenous bone chips from the maxilla for maxillary sinus floor augmentation (MSFA). Patients requiring bilateral MSFA and implant placement were enrolled in this study. Maxillary sinuses were randomly assigned (1:1) to one of the two groups: MSFA + AB (AB group) or MSFA + AB + BBM (BBM group). AB was harvested from the mandible (AB group) or locally during MSFA (BBM group). Implants were placed after 4-6 months of healing and loaded 4-6 months later. Patients were examined at baseline (1 month post-loading) and 12 months post-loading. Outcome measures included clinician- and patient-reported outcomes. Fifty patients with 198 implants were included (AB = 99, BBM = 99). Twelve implants failed before loading (AB = 6, BBM = 6), and three failed post-loading (AB = 2, BBM = 1). Implant survival was 92.9% (AB group) and 93.9% (BBM group) at 12 months post-loading, with no significant differences between the groups (HR, 0.85; 95% CI, 0.28-2.54; p = 0.770). Implant success was 98.8% (AB group) and 97.6% (BBM group), with no significant differences between the groups (HR, 0.49; 95% CI, 0.03-8.05; p = 0.620). No significant differences in marginal bone loss or clinical parameters were found between groups (p > 0.05). Patient satisfaction significantly improved at follow-ups compared to screening (p = 0.005). Implants placed after MSFA with AB or BBM showed comparable results. This indicates that MSFA can be successfully performed without requiring AB from an additional donor site, potentially reducing patient morbidity. This study was registered in the Dutch Trial Register with number NL59578.078.16 on 09-05-2017. The study start date was on 21-08-2017.

To identify changes in immunological, microbiological, and histological biomarkers that may indicate resistance during the induction phase and resilience during the resolution phase of experimental peri-implant mucositis (PiM). The search was performed in MEDLINE, EMBASE, Web of Science, SCOPUS, Cochrane Library, and LILACS databases. Prospective interventional studies assessing biomarkers during experimental PiM were included. The risk of bias was assessed using the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) tool. Meta-analyses were conducted using random-effects models. The GRADE approach was used to determine the certainty of evidence. Eleven out of 6008 studies were included. Clinical parameters (mPI and mGI) effectively characterized the experimental PiM model. Due to methodological variability and conflicting results, a definitive interpretation of microbiological and histological biomarkers was not possible. The meta-analysis revealed that IL-1β and the volume of peri-implant crevicular fluid (PICF) indicated non-resistance during the induction phase. In contrast, TNF-α, IL-6, IL-8, IL-17, MMP-8, and IFN-γ remained stable. Regarding the resolution phase, IL-1β returned to baseline levels (SMD: 1.13; 95% CI: -0.81, 3.06), and the volume of PICF (MD: 0.08; 95% CI: 0.03, 0.13) remained significantly elevated compared to day 0. However, TNF-α, IL-6, IL-8, IL-17, MMP-8, and IFN-γ did not significantly differ from baseline levels. Moderate to very low evidence suggested that the biomarkers IL-1β and the volume of PICF indicated a lack of resistance while suggesting resilience and non-resilience, respectively. The biomarkers TNF-α, IL-6, IL-8, IL-17, MMP-8, and IFN-γ demonstrated resistance and resilience.

This study evaluated ResNet-50 and U-Net models for detecting and segmenting vertical misfit in dental implant crowns using periapical radiographic images. Periapical radiographs of dental implant crowns were classified by two experts based on the presence of vertical misfit (reference group). The misfit area was manually annotated in images exhibiting vertical misfit. The resulting datasets were utilized to train the ResNet-50 and U-Net deep learning models. Then, 70% of the images were allocated for training, while the remaining 30% were used for validation and testing. Five general dentists categorized the testing images as "misfit" or "fit." Inter-rater reliability with Cohen's kappa index and performance metrics were calculated. The average performance metrics of dentists and artificial intelligence (AI) were compared using the paired-samples t test. A total of 638 radiographs were collected. The kappa values between dentists and AI ranged from 0.93 to 0.98, indicating perfect agreement. The ResNet-50 model achieved accuracy and precision of 92.7% and 87.5%, respectively, whereas dentists had a mean accuracy of 93.3% and precision of 89.6%. The sensitivity and specificity for AI were 90.3% and 93.8%, respectively, compared to 90.1% and 95.1% for dentists. The Dice coefficient yielded 88.9% for the ResNet-50 and 89.5% among the dentists. The U-Net algorithm produced a loss of 0.01 and an accuracy of 0.98. No significant difference was found between the average performance metrics of dentists and AI (p > 0.05). AI can detect and segment vertical misfit of implant prosthetic crowns in periapical radiographs, comparable to clinician performance.

ABSTRACTObjectivesPrimary stability (PS) is a key factor for promoting osseointegration and long‐term success of dental implants particularly for immediate loading protocols. Beyond the current assessments of PS, an accurate pre‐operative evaluation of PS would contribute to the improvement of surgical planning and treatment outcome. This study used biomechanical testing and homogenized finite element (hFE) analysis to objectively measure PS in the laboratory, and digitally estimate PS from prior μCT reconstructions.Material and MethodsThirty‐five bone samples extracted from the jaws of two donors were examined. Twenty‐two were finally evaluated for PS. After scanning of the samples with μCT, implants were inserted by two experienced surgeons, and various metrics such as μCT‐based bone volume fraction (BV/TV), insertion torque (IT), and resonance frequency analysis (RFA) were assessed to determine PS. Mechanical tests were conducted to measure ultimate force (UFexp) as an objective indicator of PS while the hFE simulations were performed to estimate this same ultimate force (UFsim).ResultsHigher correlation was found between UFsim and UFexp (R2 = 0.85) than between BV/TV and UFexp (R2 = 0.61), IT and UFexp (R2 = 0.50), and RFA and UFexp (R2 = 0.38). All variables demonstrated a statistically significant linear correlation with UFexp (p < 0.01).ConclusionUFsim turns out to be a more reliable and objective indicator of PS than IT and RFA. The hFE analysis requires prior μCT reconstructions and is currently limited by numerical convergence problems. Despite these limitations, pre‐operative hFE analysis emerges as a promising tool with a higher accuracy for estimation of PS than state of care techniques.

The objective of this study is to compare the effect of the location of recombinant human bone morphogenetic protein 2 (rhBMP-2) from the native bone and the periosteum for vertical alveolar bone augmentation. Mandibular, chronic, standardized, bilateral, and vertical defects in 12 beagle dogs were evaluated using four modalities: a xenograft alone (XENO; n = 6); rhBMP-2 alone (BMP; n = 6); a technique with rhBMP-2 close to the host bone covered by xenograft (SAN; n = 6); and a technique with rhBMP-2 close to the flap on top of the xenograft (LAS; n = 6). After 8 weeks, a series of invivo inspections, fluorescence microscopy, histologic and histomorphometric evaluations, and micro-CT analyses. After 8 weeks of healing, new bone formation correlated with proximity of rhBMP to the perforated membrane with BMP and LAS (p = 0.024). The highest total bone volume was found in the LAS group (45.1% ± 13.3%), followed by the SAN group (35.2% ± 6.7%), BMP group (33.1% ± 11.8%), followed by the XENO group (23.1% ± 6.5%). The SAN group demonstrated frequent seroma formation. Blood vessel formation was more pronounced in the LAS + rhBMP group, with a significant increase of 27.1% compared to the XENO group (p = 0.02). Micro-CT revealed a strong trend for higher bone volume in the BMP group (34.7%) compared to the XENO group (13.6%) (p = 0.06). Only rhBMP-2 groups demonstrated bone formation above the perforated membrane. The location of rhBMP-2 in relation to the biomaterial and periosteum influenced the effectiveness of vertical bone regeneration.