Quality of dental implants

Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.

Jumping gap in immediate implant placement in the esthetic zone: A virtual implant planning using cone-beam computed tomography

PURPOSE

A jumping gap (JG) refers to the implant's future buccal wall; this study aims to estimate the jumping gap dimension in relation to virtual implant placement and subsequently link the implant diameter and implant position with the anatomical site.

METHODS

This observational study was conducted to analyze the maxillary teeth in the esthetic zone from cone-beam computed tomography (CBCT) scans using OnDemand software. The horizontal jumping gap dimension of each tooth was assessed by subtracting the calculated virtual implant diameter from the socket dimensions.

RESULTS

A total of 253 anterior and premolar maxillary teeth were analyzed from 52 CBCT scans. The estimated JG dimensions were 1.23 ± 0.59 mm, 1.80 ± 0.64 mm, 3.02 ± 0.69 mm, for central incisors, lateral incisors and canines respectively, 3.70 ± 0.68 mm for the first premolars showing the highest horizontal gap and 3.19 ± 0.88 mm for second premolars. The incisors showed the smallest horizontal gap compared to the canines and premolars.

CONCLUSIONS

In terms of JG, immediate implant placement is more favorable at the canine and premolar sites. By contrast, the incisors sites should be handled with extreme caution, where the use of narrower implants is advisable when necessary.

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.

Effects of diameters of implant and abutment screw on stress distribution within dental implant and alveolar bone: A three-dimensional finite element analysis

Background/purpose

Few studies have investigated the effects of abutment screw diameter in the stress of dental implants and alveolar bones under occlusal forces. In this study, we investigated how variations in implant diameter, abutment screw diameter, and bone condition affect stresses in the abutment screw, implant, and surrounding bone.

Materials and methods

Three-dimensional finite element (FE) models were fabricated for dental implants with external hex-type abutments measuring 4 and 5 mm in diameter. The models also included abutment screws measuring 2.0 and 2.5 mm in diameter. Each implant model was integrated with the mandibular bone comprising the cortical bone and four types of cancellous bone. In total, 12 finite element models were generated, subjected to three different occlusal forces, and analyzed using FE software to investigate the stress distribution of dental implant and alveolar bone.

Results

Wider implants demonstrated lower stresses in implant and bone compared with standard-diameter implants. The quality of cancellous bone has a minimal impact on the stress values of the implant, abutment screw, and cortical bone. Regardless of occlusal arrangement or quality of cancellous bone, a consistent pattern emerged: larger abutment screw diameters led to increased stress levels on the screws, while the stress levels in both cortical and cancellous bone showed comparatively minor fluctuations.

Conclusion

Wider implants tend to have better stress distribution than standard-diameter implants. The potential advantage of augmenting the abutment screw diameter is unfavorable. It may result in elevated stresses in the implant system.

Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis

STATEMENT OF PROBLEM

The evidence regarding the application of artificial intelligence (AI) in identifying dental implant systems is currently inconclusive. The available studies present varying results and methodologies, making it difficult to draw definitive conclusions.

PURPOSE

The purpose of this systematic review with meta-analysis was to comprehensively analyze and evaluate articles that investigate the application of AI in identifying and classifying dental implant systems.

MATERIAL AND METHODS

An electronic systematic review was conducted across 3 databases: MEDLINE/PubMed, Cochrane, and Scopus. Additionally, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies investigating the accuracy of AI-based diagnostic tools on dental radiographs for identifying and classifying dental implant systems and comparing the results with those obtained by expert judges using manual techniques-the search strategy encompassed articles published until September 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included articles.

RESULTS

Twenty-two eligible articles were included in this review. These articles described the use of AI in detecting dental implants through conventional radiographs. The pooled data showed that dental implant identification had an overall accuracy of 92.56% (range 90.49% to 94.63%). Eleven studies showed a low risk of bias, 6 demonstrated some concern risk, and 5 showed a high risk of bias.

CONCLUSIONS

AI models using panoramic and periapical radiographs can accurately identify and categorize dental implant systems. However, additional well-conducted research is recommended to identify the most common implant systems.

Complete-arch implant-supported fixed dental prostheses fabricated with PEEK and PEKK framework: a systematic review

PURPOSE

To evaluate the performance of complete-arch implant-supported fixed dental prostheses (FDPs) fabricated with polyetheretherketone (PEEK) and polyetherketoneketone (PEKK) framework in clinical cases.

MATERIALS AND METHODS

This systematic review followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses and was registered in the International Prospective Register of Systematic Reviews with the number CRD42023399494. The electronic database PubMed, Cochrane Library and EBSCOhost were assessed for clinical research and reports on complete-arch implant-supported FDPs fabricated with PEEK and PEKK framework. Human studies with a minimum follow-up of 1 year and published in an English language were the only ones included.

RESULTS

The initial database and hand search provided 564 articles. Finally, 12 articles published between 2018 and 2022 were included in this systematic review. The mean follow-up ranged from 1 year to 6 years. The included studies reported 119 (114 PEEK, 5 PEKK) complete-arch implant-supported FDPs during 1 year follow-up. The cumulative survival rate of prostheses with PEEK as a framework was 97.3%. Prostheses fractures and complications were found with both PEEK and PEKK frameworks. No implant failure reported with both PEEK and PEKK prostheses.

CONCLUSION

In short-term follow-up, the complete-arch implant-supported FDPs with PEEK as a framework showed a good survival rate and acceptable health of the supporting tissues. The PEEK framework had shown adhesion issues as the most common prosthetic complication. Limited data were available on PEKK as framework material, so further long-term clinical trials are required.

Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study

BACKGROUND

This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.

METHODS

Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.

RESULTS

The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.

CONCLUSION

Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.

The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.

Artificial intelligence applications in implant dentistry: A systematic review

STATEMENT OF PROBLEM

Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed.

PURPOSE

The purpose of this systematic review was to assess the performance of AI models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models.

MATERIAL AND METHODS

An electronic systematic review was completed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peer-reviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus.

RESULTS

Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface.

CONCLUSIONS

AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed.

Evaluation of The Fracture Resistance and Failure Types of Different CAD/CAM Ceramic Crowns Supported by Angled Titanium Abutment

PURPOSE

To evaluate the fatigue resistance of CAD-CAM single-ceramic crowns which were applied on angled implant abutments after thermomechanical aging.

MATERIALS AND METHODS

Titanium abutments (N = 72, MODE Medical Dental Implant, Turkey) with three different angles [0˚, 15°, 25°] were restored using different materials [Monolithic zirconia (Zir), lithium silicate ceramic reinforced by zirconia (VS), and Hybrid ceramic (VE)]. Crowns in the maxillary first premolar form were cemented to abutments using resin cement (Panavia 2.0 Introkit). Dynamic loading and thermomechanical aging were applied to the specimens (120,000 cycles, 49 N, 5°C to 55°C). Fracture resistance values were measured in the universal test machine and fracture types were determined. Two-way ANOVA and Tukey test were used for statistical analysis (Jamovi version 2.3.5).

RESULTS

Both the abutment angle and the type of material had a significant effect on fracture resistance (F = 3.295, p<0.05). The highest fracture resistance was obtained in the Group 0˚-Zir, and the lowest fracture resistance was obtained in the Group 15˚-VE. Fracture resistance showed significant differences between Group 0˚-Group15˚ for the Zir and VE materials, and between Group0˚-Group25˚ for VS (p<0.05), and no statistical significance was determined between the other groups (p>0.05). When failure types were evaluated they were seen to be full or partial crown fractures, and abutment deformation was found in some samples.

CONCLUSION

Monolithic crowns may be preferred on angled abutments. The fracture resistance of CAD-CAM materials decreases as the angle of abutments increases. Monolithic zirconia has higher fracture resistance than other materials. This article is protected by copyright. All rights reserved.

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.

Distribution of implant failure caused by positioning in a certain part of Turkish society on CBCT

Purpose: The aim of this report was to evaluate the prevalence of implant failure rates due to implant positioning on Cone beam Computerized Tomography. Methods: Study sample (n= 333) consisted of CBCT(Cone-beam computerized tomography) scans of patients who were referred to the Department of Dentomaxillofacial Radiology, University of Health Sciences Turkey, Gülhane Faculty of Dentistry, Ankara, Turkey. Obtained data such as age, gender, number of implants and locations from CBCT images gathered and recorded. Results: The data consists of 333 patients and so the total data evaluated was 844. The implant survival rate of the patients between 20-40 years old (49.4%) was lower significantly than that of the patients ≥ 40 years old (P=0.001). In the R4 (right mandibular region), implant failure rate is 17.5% shows quite low rate compared to other regions. At the R1 (right maxillar region) (39%) and R2 (45%) the most common reason of failure was maxillary sinus perforation, the least common reason was palatinal bone perforation, respectively 4% and 1%. Conclusion: Preventing misinterpretations of clinicians is only possible by correct evaluation of incidental findings and better knowledge of head and neck anatomy.

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Purpose

This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures.

Materials and Methods

Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy.

Results

When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy.

Conclusion

Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

Effect of Temperature on Electrochemically Assisted Deposition and Bioactivity of CaP Coatings on CpTi Grade 4

Calcium phosphate (CaP) coatings are able to improve the osseointegration process due to their chemical composition similar to that of bone tissues. Among the methods of producing CaP coatings, the electrochemically assisted deposition (ECAD) is particularly important due to high repeatability and the possibility of deposition at room temperature and neutral pH, which allows for the co-deposition of inorganic and organic components. In this work, the ECAD of CaP coatings from an acetate bath with a Ca:P ratio of 1.67, was developed. The effect of the ECAD conditions on CaP coatings deposited on commercially pure titanium grade 4 (CpTi G4) subjected to sandblasting and autoclaving was presented. The physicochemical characteristics of the ECAD-derived coatings was carried out using SEM, EDS, FTIR, 2D roughness profiles, and amplitude sensitive eddy current method. It was showed that amorphous calcium phosphate (ACP) coatings can be obtained at a potential −1.5 to −10 V for 10 to 60 min at 20 to 70 °C. The thickness and surface roughness of the ACP coatings were an increasing function of potential, time, and temperature. The obtained ACP coatings are a precursor in the process of apatite formation in a simulated body fluid. The optimal ACP coating for use in dentistry was deposited at a potential of −3 V for 30 min at 20 °C.

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

The effects of titanium topography and chemical composition on human osteoblast cell

The objective of this study was to evaluate and compare titanium surfaces: machined (MA); sintered ceramic-blasted (HAS); sintered ceramic-blasted and acid-etched (HAS DE) and to determine the effects of surface topography, roughness and chemical composition on human osteoblast cell reaction. Titanium surface samples were analyzed with respect to surface chemical composition, topography, and roughness. The effects of material surface characteristics on osteoblasts was examined by analyzing osteoblast morphology, viability and differentiation. Osteoblasts cultured on these materials had attached, spread and proliferated on every sample. The viability of osteoblasts cultured on HAS and HAS DE samples increased more intensively in time comparing to MA sample. The viability of osteoblast cultured on HAS samples increased more intensively in the early phases of culture while for cells cultured on HAS DE the cells viability increased later in time. Alkaline phosphate activity was the highest for the cells cultured on HAS sample and statistically higher than for the MA sample. The least activity occurred on the smooth MA sample along with the rougher HAS DE samples. All the examined samples were found to be biocompatible, as indicated by cell attachment, proliferation, and differentiation. Titanium surfaces modification improved the dynamics of osteoblast viability increase. Osteoblast differentiation was found to be affected by the etching procedure and presence of Ca and P on the surface.

Old is Gold: Electrolyte Aging Influences the Topography, Chemistry, and Bioactivity of Anodized TiO2 Nanopores

Titanium dioxide (TiO₂) nanostructures including nanopores and nanotubes have been fabricated on titanium (Ti)-based orthopedic/dental implants via electrochemical anodization (EA) to enable local drug release and enhanced bioactivity. EA using organic electrolytes such as ethylene glycol often requires aging (repeated anodization of nontarget Ti) to fabricate stable well-ordered nanotopographies. However, limited information is available with respect to its influence on topography, chemistry, mechanical stability, and bioactivity of the fabricated structures. In the current study, titania nanopores (TNPs) using a similar voltage/time were fabricated using different ages of electrolyte (fresh/0 h to 30 h aged). Current density vs time plots of EA, changes in the electrolyte (pH, conductivity, and Ti/F ion concentration), and topographical, chemical, and mechanical characteristics of the fabricated TNPs were compared. EA using 10-20 h electrolytes resulted in stable TNPs with uniform size and improved alignment (parallel to the underlying substrate microroughness). Additionally, to evaluate bioactivity, primary human gingival fibroblasts (hGFs) were cultured onto various TNPs in vitro. The findings confirmed that the proliferation and morphology of hGFs were enhanced on 10-20 h aged electrolyte anodized TNPs. This pioneering study systematically investigates the optimization of anodization electrolyte toward fabricating nanoporous implants with desirable characteristics.

Long-Term Assessment of the In Vitro Corrosion Resistance of Biomimetic ACP Coatings Electrodeposited from an Acetate Bath

Calcium phosphate coatings are able to improve the osseointegration process due to their chemical composition, which is similar to that of bone tissues. In this work, to increase the long-term corrosion resistance and to improve the osseointegration process of commercially pure titanium Grade 4 (CpTi G4), biomimetic amorphous calcium phosphate (ACP) coatings were electrodeposited for the first time from an acetate bath with a pH level of 7.0 and a Ca:P ratio of 1.67. ACP coatings were obtained on CpTi G4 substrate subjected to sandblasting and autoclaving using electrochemically assisted deposition at a potential of -3 V relative to the open circuit potential for 30 min at room temperature. SEM, EDS, 2D roughness profiles, amplitude-sensitive eddy current method, and Kelvin scanning probe were used for the surface characterization of the biomaterial under study. In vitro corrosion resistance tests were conducted for 21 days in artificial saliva using open circuit potential, polarization curves, and electrochemical impedance spectroscopy measurements. The passive-transpassive behavior was revealed for the obtained ACP coatings. The long-term corrosion resistance test showed a deterioration of the protective properties for CpTi G4 uncoated and coated with ACP with immersion time. The mechanism and kinetics of the pitting corrosion on the CpTi G4|TiO2|ACP coating system are discussed in detail.

A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study

In this study, the efficacy of the automated deep convolutional neural network (DCNN) was evaluated for the classification of dental implant systems (DISs) and the accuracy of the performance was compared against that of dental professionals using dental radiographic images collected from three dental hospitals. A total of 11,980 panoramic and periapical radiographic images with six different types of DISs were divided into training (n = 9584) and testing (n = 2396) datasets. To compare the accuracy of the trained automated DCNN with dental professionals (including six board-certified periodontists, eight periodontology residents, and 11 residents not specialized in periodontology), 180 images were randomly selected from the test dataset. The accuracy of the automated DCNN based on the AUC, Youden index, sensitivity, and specificity, were 0.954, 0.808, 0.955, and 0.853, respectively. The automated DCNN outperformed most of the participating dental professionals, including board-certified periodontists, periodontal residents, and residents not specialized in periodontology. The automated DCNN was highly effective in classifying similar shapes of different types of DISs based on dental radiographic images. Further studies are necessary to determine the efficacy and feasibility of applying an automated DCNN in clinical practice.

Effect of Autoclaving Time on Corrosion Resistance of Sandblasted Ti G4 in Artificial Saliva

Titanium Grade 4 (Ti G4) is the most commonly used material for dental implants due to its excellent mechanical properties, chemical stability and biocompatibility. A thin, self-passive oxide layer with protective properties to corrosion is formed on its surface. However, the spontaneous TiO₂ layer is chemically unstable. In this work, the impact of autoclaving time on corrosion resistance of Ti G4 in artificial saliva solution with pH = 7.4 at 37 °C was studied. Ti G4 was sandblasted with white Al₂O₃ particles and autoclaved for 30–120 min. SEM, EDS, 2D roughness profiles, confocal laser scanning microscopy, and a Kelvin scanning probe were used for the surface characterization of the Ti G4 under study. In vitro corrosion resistance tests were conducted using open circuit potential, polarization curves, and electrochemical impedance spectroscopy measurements. It was found that Sa parameter, electron work function, and thickness of the oxide layers, determined based on impedance measurements, increased after autoclaving. The capacitive behavior and high corrosion resistance of tested materials were revealed. The improvement in the corrosion resistance after autoclaving was due to the presence of oxide layers with high chemical stability. The optimal Ti G4 surface for dentistry can be obtained by sandblasting with Al₂O₃ with an average grain size of 53 µm, followed by autoclaving for 90 min.

Deep Neural Networks for Dental Implant System Classification

In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study

Convolutional neural networks (CNNs), a particular type of deep learning architecture, are positioned to become one of the most transformative technologies for medical applications. The aim of the current study was to evaluate the efficacy of deep CNN algorithm for the identification and classification of dental implant systems.A total of 5390 panoramic and 5380 periapical radiographic images from 3 types of dental implant systems, with similar shape and internal conical connection, were randomly divided into training and validation dataset (80%) and a test dataset (20%). We performed image preprocessing and transfer learning techniques, based on fine-tuned and pre-trained deep CNN architecture (GoogLeNet Inception-v3). The test dataset was used to assess the accuracy, sensitivity, specificity, receiver operating characteristic curve, area under the receiver operating characteristic curve (AUC), and confusion matrix compared between deep CNN and periodontal specialist.We found that the deep CNN architecture (AUC = 0.971, 95% confidence interval 0.963-0.978) and board-certified periodontist (AUC = 0.925, 95% confidence interval 0.913-0.935) showed reliable classification accuracies.This study demonstrated that deep CNN architecture is useful for the identification and classification of dental implant systems using panoramic and periapical radiographic images.

Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs

In the absence of accurate medical records, it is critical to correctly classify implant fixture systems using periapical radiographs to provide accurate diagnoses and treatments to patients or to respond to complications. The purpose of this study was to evaluate whether deep neural networks can identify four different types of implants on intraoral radiographs. In this study, images of 801 patients who underwent periapical radiographs between 2005 and 2019 at Yonsei University Dental Hospital were used. Images containing the following four types of implants were selected: Brånemark Mk TiUnite, Dentium Implantium, Straumann Bone Level, and Straumann Tissue Level. SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50 were tested to determine the optimal pre-trained network architecture. The accuracy, precision, recall, and F1 score were calculated for each network using a confusion matrix. All five models showed a test accuracy exceeding 90%. SqueezeNet and MobileNet-v2, which are small networks with less than four million parameters, showed an accuracy of approximately 96% and 97%, respectively. The results of this study confirmed that convolutional neural networks can classify the four implant fixtures with high accuracy even with a relatively small network and a small number of images. This may solve the inconveniences associated with unnecessary treatments and medical expenses caused by lack of knowledge about the exact type of implant.

Evaluation of human osteoblast metabolic activity in modified titanium-conditioned medium

To evaluate human osteoblast metabolic activity cultured in medium conditioned with commercially pure titanium after surface treatments with alumina or ceramic grit-blasting followed by acid etching. Commercially available, pure Grade 4 titanium disks were used and subjected to seven different surface modifications: (1) machined (MA)-used as the control group; (2) blasted with Al₂O₃ (Al₂O₃); (3) blasted with sintered ceramic (HAS); (4) blasted with non-sintered ceramics (HA); (5) blasted with Al₂O₃ and etched with HCl/H₂SO₄ (Al₂O₃ DE); (6) blasted with sintered ceramic and etched with HCl/H₂SO₄ (HAS DE), and (7) blasted with non-sintered ceramic and etched with HCl/H₂SO₄ (HA DE). A samples roughness evaluation test was carried out with an interference microscope, and energy-dispersive X-ray spectroscopy was performed to evaluate the presence of aluminum, phosphorus, and calcium deposited during the titanium surface treatment along with carbon contaminants acquired by the surface during processing. A culture medium conditioned with the respective samples was prepared in five dilutions, and its effect on human osteoblast cell viability was evaluated using the relative viability of cells. Human osteoblast metabolic activity was found to be the most intensive for the Al₂O₃ DE sample. The lowest activity was observed for the HAS DE. The material's cytocompatibility depended on both the surface roughness and its chemical composition. Etching had a dual effect on cell activity, depending on the chemical composition of the titanium surface after blasting.

Use of the Sol–Gel Method for the Preparation of Coatings of Titanium Substrates with Hydroxyapatite for Biomedical Application

Hydroxyapatite (HA) was coated onto the surface of commercially pure titanium grade 4 (a material generally used for implant application) by a dip coating method using HA sol. Hydroxyapatite sol was synthesized via sol–gel using Ca(NO3)2∙4H2O and P2O5 as precursors. The surface of the HA coating was homogeneous, as determined by scanning electron microscopy (SEM), attenuated total reflectance Fourier transform infrared (ATR-FTIR), and X-ray diffraction (XRD), which allowed the materials to be characterized. The bioactivity of the synthesized materials and their efficiency for use as future bone implants was confirmed by observing the formation of a layer of hydroxyapatite on the surface of the samples soaked in a fluid simulating the composition of human blood plasma. To verify the biocompatibility of the obtained biomaterial, fibroblasts were grown on a glass surface and were tested for viability after 24 h. The results of the WST-8 analysis suggest that the HA systems, prepared by the sol–gel method, are most suitable for modifying the surface of titanium implants and improving their biocompatibility.

Evaluation of crestal bone resorption around cylindrical and conical implants following 6 months of loading: A randomized clinical trial

OBJECTIVE

The purpose of this clinical study was to evaluate the effect of implant body form (cylindrical and conical implants) on crestal bone levels during 6 months' follow-up after loading.

MATERIALS AND METHODS

A total of 32 SPI implants (19 conical implants/13 cylindrical implants) were randomly placed in 12 male patients using a submerged approach. None of the patients had compromising medical conditions or parafunctional habits. Periapical radiographs using the parallel technique were taken after clinical loading and 6 months later. Clinical indices including pocket depth and bleeding on probing (BOP) were recorded on 6-month follow-up. Data were analyzed by independent samples t-test and Chi-square test with a significance level of 0.05.

RESULTS

Six months after loading, crestal bone loss was 0.84 (±0.29) mm around the cylindrical implants and 0.73 (±0.62) mm around the conical types, which was not significantly different (P = 0.54). Pocket depth around the cylindrical and conical implants was 2.61 (±0.45) mm and 2.36 (±0.44) mm, respectively (P = 0.13). BOP was observed among 53.8% and 47.4% of the cylindrical implants and conical (P = 0.13). Bone loss and pocket depth in the maxilla and mandible had no significant difference (P = 0.46 and P = 0.09, respectively).

CONCLUSION

In this study, although bone loss and clinical parameters were slightly higher in the cylindrical implants, there was no significant difference between the conical- and cylindrical-shaped implants.

Systematic review of clinical and patient-reported outcomes following oral rehabilitation on dental implants with a tapered compared to a non-tapered implant design

BACKGROUND

Dental implants are available in different shapes.

AIMS

This systematic review aims to address whether tapered compared to non-tapered implants demonstrate similar clinical and patient-reported outcomes. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) format.

MATERIALS & METHODS

We searched electronic databases including MEDLINE through PubMed and the Cochrane Central Register of Controlled Trials for randomized clinical trials (RCT) that compare tapered versus non-tapered implants with at least 10 treated participants and a minimum mean follow-up time of 3 years. There were no restrictions to a particular treatment indication or outcome measures. Two authors independently conducted screening, risk of bias assessment, and data extraction of eligible trials in duplicate. We applied the Cochrane risk of bias assessment tool to consider risk of bias.

RESULTS

We identified 18 different RCTs, of which three reported outcomes at 3 years or greater. The three trials described the results of 245 participants with 388 implants at 3 years, from the initially 306 participants with 494 implants at baseline. The three trials compared, respectively, two, two, and three different commercially available implant brands and reported only clinically insignificant differences. We judged all three trials to be at moderate risk of bias. The low number and heterogeneity of RCTs did not allow for meta-analyses.

DISCUSSION AND CONCLUSION

Appropriate professional judgment in clinical decision making must include a comprehensive diagnosis of the patient's jawbone quality and quantity and consideration of osteotomy protocol in accordance with the patient's treatment preferences, where the shape of the dental implant is only one contributory factor.

Tailoring the immuno-responsiveness of anodized nano-engineered titanium implants

Owing to its biocompatibility and corrosion resistance, titanium is one of the most commonly used implantable biomaterials. Numerous in vitro and in vivo investigations have established that titanium surfaces with a nanoscale topography outperform conventional smooth or micro-rough surfaces in terms of achieving desirable bonding with bone (i.e. enhanced bioactivity). Among these nanoscale topographical modifications, ordered nanostructures fabricated via electrochemical anodization, especially titania nanotubes (TNTs), are particularly attractive. This is due to their ability to augment bioactivity, deliver drugs and the potential for easy/cost-effective translation into the current implant market. However, the potential of TNT-modified implants to modulate the host immune-inflammatory response, which is critical for achieving timely osseointegration, remains relatively unexplored. Such immunomodulatory effects may be achieved by modifying the physical and chemical properties of the TNTs. Furthermore, therapeutic/bioactive enhancements performed on these nano-engineered implants (such as antibacterial or osteogenic functions) are likely to illicit an immune response which needs to be appropriately controlled. The lack of sufficient in-depth studies with respect to immune cell responses to TNTs has created research gaps that must be addressed in order to facilitate the design of the next generation of immuno-modulatory titanium implants. This review article focuses on the chemical, topographical and mechanical features of TNT-modified implants that can be manipulated in order to achieve immuno-modulation, as well as providing an insight into how modulating the immune response can augment implant performance.

Mechanical Complications Related to the Retention Screws of Prefabricated Metal Abutments With Different Angulations: A Retrospective Study With 916 Implants

OBJECTIVES

The present retrospective study assessed the clinical performance of abutment screws from prefabricated metal abutments and compared technical complication rates between straight and angled abutments.

MATERIALS AND METHODS

Dental charts were selected for patients with dental implant rehabilitations delivered between 1998 and 2012. Abutment angulation, prosthetic screw type, and presence of complications that occurred during the selected time period were collected. Technical complications registered included abutment screw loosening and/or fractures detected during clinical and radiographic examinations. The chi-square test was used for statistical analysis.

RESULTS

Abutment angulations were divided into 2 groups: G1) prefabricated straight abutments and G2) prefabricated angled conical mini UCLA-type abutments. A total of 916 implants (799 straight and 117 angled conical mini UCLA-type abutments) were evaluated. G1 showed 91.1% had absence of failures, which were clinically defined as any screw loosening or fracture; and 8.9% reported some type of technical complication. G2 showed 92.3% and 7.7%, with and without technical complications, respectively.

CONCLUSIONS

No significant differences were observed between abutment angulation and technical complications.

Investigation of osteogenic responses of Fe-incorporated micro/nano-hierarchical structures on titanium surfaces

Fe incorporated micro/nano topographical titanium substrates are fabricated to synergistically regulate osteogenic responses in vitro and osseointegration in vivo.

Dentists' Most Common Practices when Selecting an Implant System

PURPOSE

To report a comprehensive description of dental implant system selection practices among dentists practicing implantology worldwide.

MATERIALS AND METHODS

An online questionnaire was designed and sent to members of 15 dental implant organizations. The survey questions addressed: dental implant system selection criteria, implant design variables, dentists' perspective to implant quality stamps, and dentists' satisfaction with their implant system(s). Responses were compiled and analyzed to determine correlation of responses using the chi-squared test (level of significance α ≤ 0.05).

RESULTS

Out of 4264 invitations sent, a total of 2001 (response rate = 46.9%) dentists participated in the survey. Approximately half of survey respondents (48.7%) were general dentists. More than two-thirds of the survey respondents (72.5%) were performing both the surgical and prosthetic implant phases. Implant-abutment connections were the most important dental implant system selection criterion (84.7%), followed by scientific evidence available on the implant system (82.8%), and simplicity of prosthetic steps (81.4%). Patient preferences (19.8%) were rated as the least important aspect. Sandblasted large gritted acid etched implant surfaces (SLA) were the most commonly used implant surfaces (75.8%); fluoride coated surfaces were the least commonly used (15.4%).

CONCLUSION

According to the results of this survey, most survey respondents practiced both surgical and prosthetic phases of dental implantology. The majority of survey respondents agreed on the importance of implant-abutment connections, scientific evidence available on implant systems, and simplicity of prosthetic steps when selecting implant systems.

The anti-bacterial activity of titanium-copper sintered alloy against Porphyromonas gingivalis in vitro

This study investigates the anti-bacterial property of Ti-Cu sintered alloys against Porphyromonas gingivalis. The anti-anaerobic property of Ti-Cu sintered alloys against P. gingivalis was investigated by antibacterial activity test, DNA measurement, DAPI staining and morphology observation. The antibacterial rates of the Ti-5Cu against P. gingivalis after 18 and 24 h incubation were 36.04 and 54.39%, and those of Ti-10Cu were 68.69 and 75.39%, which were lower than their anti-aerobic abilities. The concentration of P. gingivalis DNA gradually decreased with the increasing Cu content, which was nearly 50% after 24 h incubation on Ti-10Cu. SEM results showed that the shape of P. gingivalis changed and the bacteria broke apart with the addition of Cu and the extension of the culture time. Ti-Cu sintered alloys could not only kill anaerobic bacteria but also reduce the activity of the survived bacteria. The anti-anaerobic mechanism was thought to be in associated with the Cu ion released from Ti-Cu alloy.

Leakage of Microbial Endotoxin through the Implant-Abutment Interface in Oral Implants: An In Vitro Study

Background. Endotoxin initiates osteoclastic activity resulting in bone loss. Endotoxin leakage through implant abutment connections negatively influences peri-implant bone levels. Objectives. (i) To determine if endotoxin can traverse different implant-abutment connection (IAC) designs; (ii) to quantify the amount of endotoxins traversing the IAC; (iii) to compare the in vitro comportments of different IACs. Materials and Methods. Twenty-seven IACs were inoculated with E. coli endotoxin. Six of the twenty-seven IACs were external connections from one system (Southern Implants) and the remaining twenty-one IACs were made up of seven internal IAC types from four different implant companies (Straumann, Ankylos, and Neodent, Southern Implants). Results. Of the 27 IACs tested, all 6 external IACs leaked measurable amounts of endotoxin. Of the remaining 21 internal IACs, 9 IACs did not show measurable leakage whilst the remaining 12 IACs leaked varying amounts. The mean log endotoxin level was significantly higher for the external compared to internal types (p = 0.015). Conclusion. Within the parameters of this study, we can conclude that endotoxin leakage is dependent on the design of the IAC. Straumann Synocta, Straumann Cross-fit, and Ankylos displayed the best performances of all IACs tested with undetectable leakage after 7 days. Each of these IACs incorporated a morse-like component in their design. Speculation still exists over the impact of IAC endotoxin leakage on peri-implant tissues in vivo; hence, further investigations are required to further explore this.

Simplifying the Treatment of Bone Atrophy in the Posterior Regions: Combination of Zygomatic and Wide-Short Implants-A Case Report with 2 Years of Follow-Up

The rehabilitation of maxillary and mandibular bone atrophy represents one of the main challenges of modern oral implantology because it requires a variety of procedures, which not only differ technically, but also differ in their results. In the face of limitations such as deficiencies in the height and thickness of the alveolar structure, prosthetic rehabilitation has sought to avoid large bone reconstruction through bone grafting; this clinical behavior has become a treatment system based on evidence from clinical scientific research. In the treatment of atrophic maxilla, the use of zygomatic implants has been safely applied as a result of extreme technical rigor and mastery of this surgical skill. For cases of posterior mandibular atrophy, short implants with a large diameter and a combination of short and long implants have been recommended to improve biomechanical resistance. These surgical alternatives have demonstrated a success rate similar to that of oral rehabilitation with the placing of conventional implants, allowing the adoption of immediate loading protocol, a decrease in morbidity, simplification and speed of the treatment, and cost reduction. This case report presents complete oral rehabilitation in a patient with bilateral bone atrophy in the posterior regions of the maxilla and mandible with the goal of developing and increasing posterior occlusal stability during immediate loading.

Temperature rise during removal of fractured components out of the implant body: an in vitro study comparing two ultrasonic devices and five implant types

BACKGROUND

Ultrasonic instrumentation under magnification may facilitate mobilization of screw remnants but may induce heat trauma to surrounding bone. An increase of 5°C is considered detrimental to osseointegration. The objective of this investigation was to examine the rise in temperature of the outer implant body after 30 s of ultrasonic instrumentation to the inner part, in relation to implant type, type of ultrasonic equipment, and the use of coolants in vitro.

METHODS

Two ultrasonic devices (Satelec Suprasson T Max and Electro Medical Systems (EMS) miniMaster) were used on five different implant types that were provided with a thermo couple (Astra 3.5 mm, bone level Regular CrossFit (RC) 4.1 mm, bone level Narrow CrossFit (NC) 3.3 mm, Straumann tissue level regular body regular neck 3.3 mm, and Straumann tissue level wide body regular neck 4.8 mm), either with or without cooling during 30 s. Temperature rise at this point in time is the primary outcome measure. In addition, the mean maximum rise in temperature (all implants combined) was assessed and statistically compared among devices, implant systems, and cooling mode (independent t-tests, ANOVA, and post hoc analysis).

RESULTS

The Satelec device without cooling induces the highest temperature change of up to 13°C, particularly in both bone level implants (p < 0.05) but appears safe for approximately 10 s of continuous instrumentation, after which a cooling down period is rational. Cooling is effective for both devices. However, when the Satelec device is used with coolant for a longer period of time, a rise in temperature must be anticipated after cessation of instrumentation, and post-operational cooling is advised.

CONCLUSIONS

The in vitro setup used in this experiment implies that care should be taken when translating the observations to clinical recommendations, but it is carefully suggested that the EMS device causes limited rise in temperature, even without coolant.