Clinical Implant Dentistry and Related Research最新文献

Introduction

This is a comprehensive, long-term follow-up study of single-implant treatments. At the Brånemark Clinic in Gothenburg, Sweden, during the period of 1982–1985, 16 patients received single-tooth implants.

Objective

This study evaluates the survival rate of the implants after nearly four decades, focusing on the biological and technical complications.

Methods

Of the original 16 patients with a total of 23 implants, 13 patients with 18 implants were available for the follow-up and were included in the study. Clinical and radiographic examinations were performed on these patients.

Results

The cumulative survival rates were 95.6% for the implants and 60.9% for the implant-supported crowns after 38–40 years in function. The marginal bone level changes were 0.9 ± 1.0 mm (range, −0.5─3.0 mm) over the follow-up period.

The frequency of biological complications was low; although mucositis was common, no cases of peri-implantitis were observed. The mean plaque index was 16.9% ± 11.6% (range, 1%─34%) and the mean probing depth around the implants was 3.8 ± 2.2 mm (range, 0.0─7.0 mm). Few technical complications were observed, although many of the original implant-supported crowns had been replaced for esthetic reasons.

Conclusion

The findings emphasize the importance of long-term follow-up in implant dentistry, particularly for younger patients, to improve understanding of potential complications and the longevity of treatment outcomes. Overall, single-tooth implants have a favorable long-term prognosis, though crown replacement may eventually be necessary.

Objective

Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis.

Methods

This study utilized 45 136 maxillary sinus images from CBCT scans of 541 patients. A cascade network was designed, comprising a semi-supervised maxillary sinus area object detection module and a maxillary sinus lesions classification module. The object detection module employed a semi-supervised pseudo-labelling training strategy to expand the maxillary sinus annotation dataset. In the classification module, the performance of Convolutional Neural Network and Transformer architectures was compared for maxillary sinus mucosal lesion classification. The object detection and classification modules were evaluated using metrics including Accuracy, Precision, Recall, F1 score, and Average Precision, with the object detection module additionally assessed using Precision-Recall Curve.

Results

The fully supervised pseudo-label generation model achieved an average accuracy of 0.9433, while the semi-supervised maxillary sinus detection model attained 0.9403. ResNet-50 outperformed in classification, with accuracies of 0.9836 (sagittal) and 0.9797 (coronal). Grad-CAM visualization confirmed accurate focus on clinically relevant lesion features.

Conclusion

The proposed pipeline achieves high-precision detection and classification of maxillary sinus mucosal lesions, reducing manual annotation while maintaining accuracy.

Objectives

This study aimed to evaluate the impact of implant placement with simultaneous bone augmentation on patients' willingness to repeat the procedure. Secondary outcomes included assessing patient-reported outcome measures, clinical- and surgical-related factors affecting treatment outcomes.

Methods

Subjects who were currently undergoing or have undergone implant placement with simultaneous bone augmentation in the esthetic region (maxillary second premolar to second premolar) were assigned to three groups: Group A (short-term), Group B (1–5 years post-surgery), and Group C (> 5 years post-surgery). Clinical assessments, dichotomous questions, and visual analogue scales were used to evaluate therapeutic outcomes according to the group of interest.

Results

One hundred and fifty patients completed the study (n = 50 per group). In Group A, 16% experienced wound dehiscence (membrane exposure < 3 mm), and 2% had postoperative bleeding, infection, swelling, or abscess. Flaps extending beyond three teeth significantly increased early membrane exposure (p = 0.04). Patient-reported postoperative discomfort (p < 0.001) and wound healing scores (p < 0.04) decreased over time. Willingness to repeat the procedure was 100%, 88%, and 98% in Groups A, B, and C, respectively, with satisfaction rates of 98.2 ± 7.5, 91.2 ± 10.6, and 95.2 ± 10.9. No significant differences were found between Groups B and C for functional or esthetic satisfaction. However, Group C reported higher satisfaction and comfort (p < 0.001). Younger adults and women reported greater postoperative discomfort than older adults and men.

Conclusions

Implant placement with simultaneous horizontal bone augmentation in esthetic areas shows a high willingness of patients to repeat the procedure in the short-, mid-, and long-term. Additionally, this therapeutic approach yields low rates of postoperative complications and discomfort, along with high patient satisfaction related to functional performance, esthetics, and overall comfort.

Objectives

To evaluate the dimensional reduction of the peri-implant hard tissues and esthetic outcome after immediate implant placement (IIP) in the esthetic zone.

Material and Methods

Patients who received IIP with bone grafts in the esthetic zone, with either immediate or delayed restoration, were included in this retrospective cohort study. The implants were categorized into three groups based on the labial bone thickness (LBT) before implantation: Group 1 (≤ 0.5 mm), Group 2 (0.5–1 mm), Group 3 (≥ 1 mm). The horizontal bone loss (HBL) at 0, 3, 5 mm apical to implant shoulder, peri-implant marginal bone loss (MBL), and Pink Esthetic Score (PES) were used to evaluate the hard and soft tissue after IIP.

Results

A total of 87 implants in 74 patients met the inclusion criteria. Compared to group 3, there was significant severe bone loss in the HBL-0 mm in groups 1 (p = 0.017); and the implant located in the central incisor position and female may led to increased bone resorption (p = 0.021, p = 0.061, respectively). For HBL-3 mm and HBL-5 mm, the non-immediate restoration may reduce bone resorption (p = 0.013, p = 0.022, respectively). The MBL during short-term follow-up and PES score showed no significant difference among three groups.

Conclusions

Despite limitations, our study found that LBT < 0.5 mm significantly affected horizontal bone loss. Meanwhile, immediate restoration, implant position of central incisors and female may also be considered as risk factors for HBL. However, the difference in the labial bone did not significantly affect MBL, or peri-implant soft tissue outcomes.

Trial Registration

This study was registered in a clinical trial registry (www.chictr.org.cn, No: ChiCTR2400087990)

Objective

This cross-sectional study aimed to investigate the salivary profile of inflammatory mediators in individuals with periodontal and peri-implant disease as compared to individuals with periodontal and peri-implant health.

Materials and Methods

Saliva samples were collected from 155 participants (mean age 63.3 ± 11.4 years), comprising individuals with periodontal and peri-implant health (N = 41), gingivitis and/or mucositis (N = 18), and periodontitis and/or peri-implantitis (N = 96). Samples were analyzed using multiplex-immunoassay panel consisting of inflammatory mediators in the tumor necrosis factor (TNF), interferon (IFN), interleukin (IL) superfamily, and matrix metalloproteinases.

Results

The levels of B-cell activating factor (BAFF), sIL-6Rβ, IFN-β, and sIL-6Rα, sTNFR1, and Pentraxin-3 were significantly higher in patients with periodontitis and/or peri-implantitis compared to healthy subjects. Furthermore, among the investigated inflammatory mediators, Pentraxin-3 exhibited the highest diagnostic potential (AUC = 0.74) for distinguishing between subjects with periodontitis and/or peri-implantitis and healthy individuals.

Conclusions

Our findings demonstrated elevated salivary levels of BAFF, sIL-6Rβ, IFN-β, sIL-6Rα, sTNF-R1, and Pentraxin-3 in individuals with periodontitis and/or peri-implantitis in comparison to periodontal and peri-implant healthy controls.

Background

This cross-sectional study aimed to compare the composition of the submucosal microbiome of peri-implantitis with paired and unpaired healthy implant samples.

Methods

We evaluated submucosal plaque samples obtained in 39 cases, including 13 cases of peri-implantitis, 13 cases involving healthy implants from the same patient (paired samples), and 13 cases involving healthy implants from different individuals (unpaired samples). The patients were evaluated using next-generation genomic sequencing (Illumina) based on 16S rRNA gene amplification. The sequences were grouped according to the amplicon sequence variant (ASV) to define the taxonomic categories. Alpha diversity was analyzed using Shannon's and Simpson's indices, while beta diversity was evaluated using principal coordinate analysis, analysis of similarities, and permutational multivariate variance analysis. Additionally, UniFrac distances were evaluated using Quantitative Insights into Microbial Ecology 2. Finally, we evaluated between-group differences in the taxonomic components.

Results

There were no significant between-group differences in alpha diversity. The average bacterial ratios of Filifactor alocis, Porphyromona endodontalis, Tannerella forsythia, Treponema denticola, Peptostreptococcaceae [Eubacterium nodatum], Desulfobulbus sp. HTM 041, and Mogibacterium timidum significantly differed between peri-implantitis samples and unpaired samples from the healthy implants (p < 0.05). However, there were few differences in the microbiota between peri-implantitis samples and those paired with healthy implants in the same patient.

Conclusions

Future studies comparing the microbiome compositions using sequencing techniques between healthy implants and implants with peri-implantitis should focus on retrieving samples from the same patient, especially in individuals with a history of periodontitis.

Objectives

This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.

Materials and Methods

A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized. Two deep-learning models were trained using the YOLOv8 algorithm. The first model classified the regions of the jaw to number the teeth and identify implant regions, while the second model performed implant segmentation. Performance metrics including precision, recall, and F1-score were used to evaluate the model's effectiveness.

Results

The implant segmentation model achieved a precision of 91.4%, recall of 90.5%, and an F1-score of 93.1%. For the implant-numbering task, precision ranged from 0.94 to 0.981, recall from 0.895 to 0.956, and F1-scores from 0.917 to 0.966 across various jaw regions. The analysis revealed that implants were most frequently located in the maxillary posterior region.

Conclusions

The AI model demonstrated high accuracy in detecting and numbering dental implants in panoramic radiographs. This technology offers the potential to reduce clinicians' workload and improve diagnostic accuracy in dental implantology. Further validation across more diverse datasets is recommended to enhance its clinical applicability.

Clinical Relevance

This AI model could revolutionize dental implant detection and classification, providing fast, objective analyses to support clinical decision-making in dental practices.

Purpose

The purpose of this study was to verify the effect of our previously proposed mesiodistal distance for multiple posterior implants on preserving peri-implant bone, and to provide a measurable criterion for predicting implant prognosis.

Methods

One hundred and two patients with dental implants placed in the posterior free-end edentulous arches were recruited in this cross-sectional study. Calibrated X-rays (peri-apical [PA] or bite-wing x-ray) were collected to measure the mesiodistal space as well as the corresponding bone resorption of implants after prosthesis placement. Implants were assigned to the test/control group according to whether their mesiodistal distance following our proposed algorithm. After adjusting covariates, logistic multivariate regression analyses were performed to examine the relationship between inter-implant distance and marginal bone loss (MBL) during each follow-up interval. The effect of smoking habits was also analyzed.

Results

Every observation period exhibited great significance between experimental and control group on peri-implant bone level (p = 0.006, 0.005, 0.001, 0.025, 0.001, correspondingly) and the difference had a tendency to grow as time went by except 4-year (MD = −0.19, −0.39, −0.43, −0.30, −1.26, correspondingly). While no significant difference was observed between smokers and nonsmokers in the same group (p > 0.05).

Conclusion

The mesiodistal algorithm of 4–4.6 mm (implant to adjacent canine tooth), 7–7.4 mm, 8–8.5 mm, and 9–9.5 mm was proved to be effective for maintaining peri-implant bone level. It was also observed in our study that the impact of inter-implant distance outweighed that of smoking. This study provided clinicians predictable prognostic outcomes for implants and reference for deciding treatment plans.

Objectives

This study evaluated different designs of the conical implant-abutment connection (IAC) and their resistance to microgap formation under oblique loads as specified by the ISO standard for testing dental implants. Also evaluated was the effect of deviations from the ISO specifications on the outcomes.

Methods

Finite element analysis was conducted to compare the microgap formation and stress distribution among three conical IAC designs (A, B, and C) in two loading configurations: one compliant with ISO 14801 and one with a modified load adaptor (non-ISO). The different IAC designs varied in the taper, diameter, and cone height. The cone angle mismatch (Cam) between the implant and abutment was considered. A torque of 20 Ncm and oblique loads (up to 400 N) were simulated.

Results

The stresses produced by the screw-tightening torque varied among the different IAC designs. The contact height was approximately 0.3 mm for Designs A and B, and less than 0.03 mm for Design C. Under oblique loads, Design A maintained IAC sealing without gap formation up to 400 N. With the ISO adaptor, gaps appeared in Design B at 300 N and in Design C at 90 N. The non-ISO adaptor resulted in gap formation at 160 N for Design B and at 50 N for Design C.

Conclusions

The IAC design and cone angle mismatch significantly influenced microgap formation, with some designs showing zero gaps even when the oblique load reached 400 N. The non-ISO adaptor increased gap formation in IACs B and C.

Introduction

Implantology has become a primary solution for tooth loss due to excellent osseointegration and high long-term success rates. However, complications such as abutment screw loosening, especially in implant-supported single crowns, compromise prosthesis longevity. Anaerobic adhesives (AAs) have shown promise in mechanical fields for preventing screw loosening, but their effectiveness in dental implants, particularly zirconia, remains uncertain. This study evaluates the effect of medium-strength AA on the counter-torque and screw stability of zirconia implant abutments.

Methods

Twenty neodent Zi zirconia implants were divided into two groups: control (n = 10) without adhesive and experimental (n = 10) using medium-strength AA (Loctite 242) on the prosthetic screw. Abutment screws were torqued to 32 Ncm and underwent mechanical cycling simulating mastication. Counter-torque was measured post-cycling. Structural damage and adhesive residues were inspected using a stereomicroscope. Data were analyzed using descriptive statistics including means and standard deviations (SDs). The Shapiro–Wilk test was performed to assess data normality. The counter-torque values after cycling were compared to the installation torque value using the one-sample t-test. Comparison between groups was performed using Student's t test. Statistical significance was established as p < 0.05.

Results

The presence of AA was detected on all screws, without causing damage and was easily removable. Both groups showed significantly lower counter-torque values than the installation torque, with no significant intergroup difference.

Conclusion

Medium-strength AA did not significantly affect the counter-torque values of zirconia implant abutment screws, although it was easily removable and caused no damage.