Parameters to Define Peri-Implantitis: A Review and a Proposed Multi-Domain Scale

Influence of crestal and sub-crestal implant position on development of peri-implant diseases: a 5-year retrospective analysis

OBJECTIVES

The aim of the present study was to evaluate the influence of crestal and subcrestal implant position on development of peri-implant diseases.

MATERIALS AND METHODS

The study was designed as a retrospective clinical and radiographic analysis. Implant-supported fixed dental prostheses were allocated in two groups: with the shoulder (i) placed in sub-crestal level and (ii) placed at bone level. For each patient, the following clinical variables were assessed: FMPS, FMBS, PlI, BOP, and PD. After prothesis delivery, an intraoral radiograph was obtained; this exam was performed also at 5 years of observation period.

RESULTS

No statistically significant difference was found in terms of FMPS and FMBS at baseline and after 5 years follow-up (P < 0.05). A statistically significant difference was assessed between PD of control group and test group (P = 0.042). Patient-based analysis showed a 25.6% of peri-implant mucositis and 32.6% of peri-implantitis for implants placed with the shoulder in crestal position, while for implants inserted in sub-crestal position the percentage of peri-implant-mucositis and peri-implantitis were 19%; no statistically significant difference was found between groups after 5 years (P < 0.05).

CONCLUSIONS

Within the limitation of the present study, the clinical and radiographic outcomes showed that the percentage of peri-implant mucositis and peri-implantitis was not statistically significant for both groups after 5 years follow-up.

CLINICAL RELEVANCE

The outcomes of present study clinically demonstrated that a deep position of implant shoulder did not provide any benefits. On the contrary, it may be considered a possible risk indicator for implant diseases.

Prevalence of peri-implantitis after alveolar ridge preservation at periodontitis and nonperiodontitis extraction sites: A retrospective cohort study

INTRODUCTION

Periodontitis is the main indication for dental extraction and often leads to peri-implantitis (PI). Alveolar ridge preservation (ARP) is an effective means of preserving ridge dimensions after extraction. However, whether PI prevalence is lower after ARP for extraction after periodontitis remains unclear. This study investigated PI after ARP in patients with periodontitis.

MATERIALS AND METHODS

This study explored the 138 dental implants of 113 patients. The reasons for extraction were categorized as periodontitis or nonperiodontitis. All implants were placed at sites treated using ARP. PI was diagnosed on the basis of radiographic bone loss of ≥3 mm, as determined through comparison of standardized bitewing radiographs obtained immediately after insertion with those obtained after at least 6 months. Chi-square and two-sample t testing and generalized estimating equations (GEE) logistic regression model were employed to identify risk factors for PI. Statistical significance was indicated by p < 0.05.

RESULTS

The overall PI prevalence was 24.6% (n = 34). The GEE univariate logistic regression demonstrated that implant sites and implant types were significantly associated with PI (premolar vs. molar: crude odds ratios [OR] = 5.27, 95% confidence intervals [CI] = 2.15-12.87, p = 0.0003; bone level vs. tissue level: crude OR = 5.08, 95% CI = 2.10-12.24; p = 0.003, respectively). After adjustment for confounding factors, the risks of PI were significantly associated with implant sites (premolar vs. molar: adjusted OR [AOR] = 4.62, 95% CI = 1.74-12.24; p = 0.002) and implant types (bone level vs. tissue level: AOR = 6.46, 95% CI = 1.67-25.02; p = 0.007). The reason for dental extraction-that is, periodontitis or nonperiodontitis-was not significantly associated with PI.

CONCLUSION

ARP reduces the incidence of periodontitis-related PI at extraction sites. To address the limitations of our study, consistent and prospective randomized controlled trials are warranted.

Non-Surgical Therapy and Oral Microbiota Features in Peri-Implant Complications: A Brief Narrative Review

The therapeutic discretion in cases of peri-implantitis should take into account the limits and advantages of specific therapeutic itineraries tailored according to each clinical case and each individual patient. This type of oral pathology emphasizes the complex classification and diagnostic issues coupled with the need for targeted treatments, in light of the oral peri-implant microbiota changes. This review highlights the current indications for the non-surgical treatment of peri-implantitis, describing the specific therapeutic efficacy of different approaches and discussing the more appropriate application of single non-invasive therapies The non-surgical treatment choice with antiseptics or antibiotics (single or combined, local, or systemic) for short courses should be considered on a case-by-case basis to minimize the incidence of side effects and concomitantly avoid disease progression.

Deep learning in periodontology and oral implantology: A scoping review

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs

Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (n = 508), validation (n = 100), and test (n = 100) datasets. The training dataset was randomly enriched by data augmentation. For evaluation, average precision, average recall, and mean object keypoint similarity (OKS) were calculated, and the mean OKS values of the model and a dental clinician were compared. Using detected keypoints, radiographic bone loss was measured and classified. No statistically significant difference was found between the modified R-CNN model and dental clinician for detecting landmarks around dental implants. The modified R-CNN model can be utilized to measure the radiographic peri-implant bone loss ratio to assess the severity of peri-implantitis.

Electrochemical Disinfection of Dental Implants Experimentally Contaminated with Microorganisms as a Model for Periimplantitis

Despite several methods having been described for disinfecting implants affected by periimplantitis, none of these are universally effective and may even alter surfaces and mechanical properties of implants. Boron-doped diamond (BDD) electrodes were fabricated from niobium wires and assembled as a single instrument for implant cleaning. Chemo-mechanical debridement and air abrasion were used as control methods. Different mono-species biofilms, formed by bacteria and yeasts, were allowed to develop in rich medium at 37 °C for three days. In addition, natural multi-species biofilms were treated. Implants were placed in silicone, polyurethane foam and bovine ribs for simulating different clinical conditions. Following treatment, the implants were rolled on blood agar plates, which were subsequently incubated at 37 °C and microbial growth was analyzed. Complete electrochemical disinfection of implant surfaces was achieved with a maximum treatment time of 20 min for Candida albicans, Candida dubliniensis, Enterococcus faecalis, Roseomonas mucosa, Staphylococcus epidermidis and Streptococcus sanguinis, while in case of spore-forming Bacillus pumilus and Bacillus subtilis, a number of colonies appeared after BDD electrode treatment indicating an incomplete disinfection. Independent of the species tested, complete disinfection was never achieved when conventional techniques were used. During treatment with BDD electrodes, only minor changes in temperature and pH value were observed. The instrument used here requires optimization so that higher charge quantities can be applied in shorter treatment times.