Management of Implant/Prosthodontic Complications

Prevalence and Risk Factors of Peri-Implant Disease: A Retrospective Case-Control Study in Western China

BACKGROUND

The present study aimed to investigate the prevalence of peri-implant disease and identify potential disease risk factors in western China.

METHODS

The present retrospective study was conducted in 131 consecutive patients receiving 248 dental implants treated with implant-supported prostheses with a mean follow-up of 2.52 years. Several patient-related, implant-related, and oral hygiene maintenance factors were analyzed.

RESULTS

Peri-implant disease developed in 68 (51.91%) patients and 110 (44.35%) implants. The prevalence of peri-implant mucositis and peri-implantitis were 45.80% and 7.63%, respectively, at the subject level, and 36.69% and 7.66%, respectively, at the implant level. Multivariate analysis exhibited that male [odds ratio (OR) = 1.91; 95% confidence interval (CI): 1.02-3.57; p = 0.04], implant length < 10mm (OR = 7.87; 95% CI:1.62-38.46; p = 0.01), poor proximal contact of the prosthesis (OR = 1.90; 95% CI: 1.06-3.42; p = 0.03), tooth brushing once a day (OR = 3.11; 95% CI: 1.26-7.68; p = 0.04) and moderate periodontitis (OR = 13.00; 95% CI: 4.38-38.60; p < 0.01) were independent risk factors for peri-implant disease.

Evaluation of sealing efficacy and removal convenience of sealing materials for implant abutment screw access holes

Background

Sealing materials are used to fill abutment screw access holes (SAH) to prevent microleakage and protect the central screws in oral implant restoration. However, thus far, no consensus has been reached on sealing material selection. In this study, a comparison of the sealing efficacy and removal convenience of different sealing materials for cement-retained implant restoration was conducted.

Methods

Various sealing materials were classified into five groups, namely, gutta-percha (GP), temporary restorative paste (TRP), vinyl polysiloxane (VPS), polytetrafluoroethylene (PTFE) tape, and onlay resin (OR), and 35 sets of analog-abutments were allocated into five groups of seven specimens. A sealing efficacy test was conducted using a modified dye-penetration method, in which a lower absorbance indicated better sealing efficacy. For the removal-convenience test, the materials were removed from each SAH after solidification, and the retrieval time was recorded.

Results

On days 1 and 10, PTFE exhibited the highest absorbance value with significant differences compared to the other groups. On day 30, TRP and PTFE showed significantly higher absorbance values than GP, VPS, and OR, but no significant difference was detected between TRP and PTFE (p = 0.424). The absorbance values of TRP and PTFE from days 1, 10, and 30 showed significant intragroup differences, while those of the other groups did not. In terms of the removal convenience on days 1, 10, and 30, VPS achieved the best performance, followed by PTFE, OR, TRP, and GP.

Conclusion

Within the limitations of this experiment, VPS and OR showed better sealing efficacy against microleakage and a more convenient removal than the other materials; thus, VPS and OR are recommended for clinical use.

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to “Huge” for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.

Antibacterial and Antioxidant Effects of Magnesium Alloy on Titanium Dental Implants

OBJECTIVES

In this study, a new type of dental implant by covering the surface of the titanium (Ti) implant with zinc-magnesium (Zn-Mg) alloy was designed, to study the antibacterial and antioxidant effects of Mg alloy on titanium (Ti) implants in oral implant restoration.

METHODS

Human gingival fibroblasts (HGFs), S. sanguinis, and F. nucleatum bacteria were used to detect the bioactivity and antibacterial properties of Mg alloy-coated Ti implants. In addition, B6/J mice implanted with different materials were used to further detect their antibacterial and antioxidant properties.

RESULTS

The results showed that Mg alloy could better promote the adhesion and proliferation and improve the alkaline phosphatase (ALP) activity of HGFs, which contributed to better improved stability of implant osseointegration. In addition, Mg alloy could better inhibit the proliferation of S. sanguinis, while no significant difference was found in the proliferation of F. nucleatum between the two implants. In the mouse model, the peripheral inflammatory reaction and oxidative stress of the Mg alloy implant were significantly lower than those of the Ti alloy implant.

CONCLUSIONS

Zn-Mg alloy-coated Ti implants could better inhibit the growth of Gram-positive bacteria in the oral cavity, inhibit oxidative stress, and facilitate the proliferation activity of HGFs and the potential of osteoblast differentiation, thus, better increasing the stability of implant osseointegration.

Photodynamic Therapy for Biomodulation and Disinfection in Implant Dentistry: Is It Feasible and Effective?

Dental implants are the most common rehabilitation and restorative treatment used to replace missing teeth. Biofilms adhere to implant surfaces to trigger implant-associated infection and inflammatory response. Clinically, the biofilm induces a local host response with the infiltration of phagocytic immune cells. The pro-inflammatory surroundings set off osteoclastogenesis, which leads to the septic loosening of the implant. The standard of dental care for implant-associated infection relies on a combination of surgery and antimicrobial therapy. Antimicrobial photodynamic therapy is a noninvasive and photochemistry-based approach capable of reducing bacterial load and modulating inflammatory responses. In this review, we explore the photobiomodulation and disinfection outcomes promoted by photodynamic therapy for implant infections, highlighting the quality of evidence on the most up-to-date studies, and discuss the major challenges on the advance of these therapeutic strategies.

Identification of dental implants using deep learning-pilot study

Background

In some cases, a dentist cannot solve the difficulties a patient has with an implant because the implant system is unknown. Therefore, there is a need for a system for identifying the implant system of a patient from limited data that does not depend on the dentist’s knowledge and experience. The purpose of this study was to identify dental implant systems using a deep learning method.

Methods

A dataset of 1282 panoramic radiograph images with implants were used for deep learning. An object detection algorithm (Yolov3) was used to identify the six implant systems by three manufactures. To implement the algorithm, TensorFlow and Keras deep-learning libraries were used. After training was complete, the true positive (TP) ratio and average precision (AP) of each implant system as well as the mean AP (mAP), and mean intersection over union (mIoU) were calculated to evaluate the performance of the model.

Results

The number of each implant system varied from 240 to 1919. The TP ratio and AP of each implant system varied from 0.50 to 0.82 and from 0.51 to 0.85, respectively. The mAP and mIoU of this model were 0.71 and 0.72, respectively.

Conclusions

The results of this study suggest that implants can be identified from panoramic radiographic images using deep learning-based object detection. This identification system could help dentists as well as patients suffering from implant problems. However, more images of other implant systems will be necessary to increase the learning performance to apply this system in clinical practice.

How Accurate Is Oral Implant Installation Using Surgical Guides Printed from a Degradable and Steam-Sterilized Biopolymer?

3D printed surgical guides are used for prosthetically-driven oral implant placement. When manufacturing these guides, information regarding suitable printing techniques and materials as well as the necessity for additional, non-printed stock parts such as metal sleeves is scarce. The aim of the investigation was to determine the accuracy of a surgical workflow for oral implant placement using guides manufactured by means of fused deposition modeling (FDM) from a biodegradable and sterilizable biopolymer filament. Furthermore, the potential benefit of metal sleeve inserts should be assessed. A surgical guide was designed for the installation of two implants in the region of the second premolar (SP) and second molar (SM) in a mandibular typodont model. For two additive manufacturing techniques (stereolithography [SLA]: reference group, FDM: observational group) n = 10 surgical guides, with (S) and without (NS) metal sleeves, were used. This resulted in 4 groups of 10 samples each (SLA-S/NS, FDM-S/NS). Target and real implant positions were superimposed and compared using a dedicated software. Sagittal, transversal, and vertical discrepancies at the level of the implant shoulder, apex and regarding the main axis were determined. MANOVA with posthoc Tukey tests were performed for statistical analyses. Placed implants showed sagittal and transversal discrepancies of <1 mm, vertical discrepancies of <0.6 mm, and axial deviations of ≤3°. In the vertical dimension, no differences between the four groups were measured (p ≤ 0.054). In the sagittal dimension, SLA groups showed decreased deviations in the implant shoulder region compared to FDM (p ≤ 0.033), whereas no differences in the transversal dimension between the groups were measured (p ≤ 0.054). The use of metal sleeves did not affect axial, vertical, and sagittal accuracy, but resulted in increased transversal deviations (p = 0.001). Regarding accuracy, biopolymer-based surgical guides manufactured by means of FDM present similar accuracy than SLA. Cytotoxicity tests are necessary to confirm their biocompatibility in the oral environment.

Biological Responses to the Transitional Area of Dental Implants: Material- and Structure-Dependent Responses of Peri-Implant Tissue to Abutments

The stability of peri-implant tissue is essential for the long-term success of dental implants. Although various types of implant connections are used, little is known about the effects of the physical mechanisms of dental implants on the stability of peri-implant tissue. This review summarizes the relevant literature to establish guidelines regarding the effects of connection type between abutments and implants in soft and hard tissues. Soft tissue seals can affect soft tissue around implants. In external connections, micromobility between the abutment and the hex component of the implant, resulting from machining tolerance, can destroy the soft tissue seal, potentially leading to microbial invasion. Internal friction connection implants induce strain on the surrounding bone via implant wall expansion that translates into masticatory force. This strain is advantageous because it increases the amount and quality of peri-implant bone. The comparison of internal and external connections, the two most commonly used connection types, reveals that internal friction has a positive influence on both soft and hard tissues.