Radiographic alveolar bone assessment in correlation with primary implant stability: A systematic review and meta-analysis.

INTRODUCTION: The radiographic examination of alveolar bone using 3D radiographic examination is essential in dental implant treatment planning. Our study aimed to systematically review and quantitatively analyze the correlation between alveolar bone parameters, specifically bone density and cortical bone thickness, assessed using cone beam computed tomography (CBCT) and/or multidetector computed tomography (MDCT); and primary implant stability (PIS) determined using implant stability quotient (ISQ), Periotest® value (PTV), and insertion torque value (ITV). METHODS: This review was registered in the PROSPERO database (registration number CRD42022307245). An electronic literature search was conducted on the PubMed, SCOPUS, and Web of Science databases for papers published until February 2022. The Quality Assessment in Prognostic Studies (QUIPS) tool was used to assess risk of bias. Meta-analyses were conducted to calculate the estimated average correlation coefficient based on a multilevel random-effects model, followed by subgroup analysis. RESULTS: Twenty-six studies were included in this review, consisting of 17 prospective cohort studies, eight retrospective cohort studies, and one nonrandomized controlled trial. A total of 3109 implants placed in 1171 subjects were analyzed. Twenty-three studies were evaluated using meta-analysis. The alveolar bone condition was significantly correlated with ISQ (r = 0.60; p < .001), IT (r = 0.52; p < .001), and PTV (r = -0.42; p < .05). CONCLUSION: Alveolar bone condition is significantly associated with PIS. Low bone density and thin cortical bone can lead to low PIS; therefore, modification of treatment planning and surgical procedures might be needed to avoid poor osseointegration.

Automated permanent tooth detection and numbering on panoramic radiograph using a deep learning approach.

OBJECTIVE: This study aimed to assess the performance of the deep learning (DL) model for automated tooth numbering in panoramic radiographs. STUDY DESIGN: The dataset of 500 panoramic images was selected according to the inclusion criteria and divided into training and testing data with a ratio of 80%:20%. Annotation on the data set was categorized into 32 classes based on the dental nomenclature of the universal numbering system using the LabelImg software. The training and testing process was carried out using You Only Look Once (YOLO) v4, a deep convolution neural network model for multiobject detection. The performance of YOLO v4 was evaluated using a confusion matrix. Furthermore, the detection time of YOLO v4 was compared with a certified radiologist using the Mann-Whitney test. RESULTS: The accuracy, precision, recall, and F1 scores of YOLO v4 for tooth detection and numbering in the panoramic radiograph were 88.5%, 87.70%, 100%, and 93.44%, respectively. The mean numbering time using YOLO v4 was 20.58 ± 0.29 ms, significantly faster than humans (P < .0001). CONCLUSIONS: The DL approach using the YOLO v4 model can be used to assist dentists in daily practice by performing accurate and fast automated tooth detection and numbering on panoramic radiographs.

Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Material and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

The accuracy of implant placement with computer-guided surgery in partially edentulous patients and possible influencing factors: A systematic review and meta-analysis.

PURPOSE: To review the current clinical studies regarding the accuracy of implant computer-guided surgery in partially edentulous patients and investigate potential influencing factors. STUDY SELECTION: Electronic searches on the PubMed and Cochrane Central Register of Controlled Trials databases, and subsequent manual searches were performed. Two reviewers selected the studies following our inclusion and exclusion criteria. Qualitative review and meta-analysis of the implant placement accuracy were performed to analyze potential influencing factors. Angular deviation, coronal deviation, apical deviation, and depth deviation were evaluated as the accuracy outcomes. RESULTS: Eighteen studies were included in this systematic review, including six randomized controlled trials, nine prospective studies, and three retrospective clinical studies. A total of 1317 implants placed in 642 partially edentulous patients were reviewed. Eight studies were evaluated using meta-analysis. Fully guided surgery showed statistically higher accuracy in angular (P <0.001), coronal (P <0.001), and apical deviation (P <0.05) compared with pilot-drill guided surgery. A statistically significant difference (P <0.001) was also observed in coronal deviation between the bounded edentulous (BES) and distal extension spaces (DES). A significantly lower angular deviation (P <0.001) was found in implants placed using computer-aided design/computer-aided manufacturing (CAD/CAM) compared to the conventional surgical guides. CONCLUSION: The edentulous space type, surgical guide manufacturing procedure, and guided surgery protocol can influence the accuracy of computer-guided surgery in partially edentulous patients. Higher accuracy was found when the implants were placed in BES, with CAD/CAM manufactured surgical guides, using a fully guided surgery protocol.

Current applications and development of artificial intelligence for digital dental radiography.

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

Influence of bone condition on implant placement accuracy with computer-guided surgery.

BACKGROUND: The impact of the jaw bone condition, such as bone quantity and quality in the implant placement site, affecting the accuracy of implant placement with computer-guided surgery (CGS) remains unclear. Therefore, this study aimed to evaluate the influence of bone condition, i.e., bone density, bone width, and cortical bone thickness at the crestal bone on the accuracy of implant placement with CGS. METHODS: A total of 47 tissue-level implants from 25 patients placed in the posterior mandibular area were studied. Implant placement position was planned on the simulation software, Simplant® Pro 16, by superimposing preoperative computed tomography images with stereolithography data of diagnostic wax-up on the dental cast. Implant placement surgery was performed using the surgical guide plate to reflect the planned implant position. The post-surgical dental cast was scanned to determine the position of the placed implant. Linear and vertical deviations between planned and placed implants were calculated. Deviations at both platform and apical of the implant were measured in the bucco-lingual and mesio-distal directions. Intra- and inter-observer variabilities were calculated to ensure measurement reliability. Multiple linear regression analysis was employed to investigate the effect of the bone condition, such as density, width, and cortical bone thickness at the implant site area, on the accuracy of implant placement (α = 0.05). RESULT: Intra- and inter-observer variabilities of these measurements showed excellent agreement (intra class correlation coefficient ± 0.90). Bone condition significantly influenced the accuracy of implant placement using CGS (p < 0.05). Both bone density and width were found to be significant predictors. CONCLUSIONS: Low bone density and/or narrow bucco-lingual width near the alveolar bone crest in the implant placement site might be a risk factor influencing the accuracy of implant placement with CGS.

Xerostomia Therapy Due to Ionized Radiation Using Preconditioned Bone Marrow-Derived Mesenchymal Stem Cells.

OBJECTIVES: The aim of this study was to describe the process of regeneration of damaged salivary glands due to ionizing radiations by bone marrow mesenchymal stem cells (BM-MSCs) transplantation that have been given hypoxic preconditioning with 1% O2 concentration. MATERIALS AND METHODS: Stem cell culture was performed under normoxic (O2: 21%) and hypoxic conditions by incubating the cells for 48 hours in a low oxygen tension chamber consisting of 95% N2, 5% CO2, and 1% O2. Thirty male Wistar rats were divided into four groups: two groups of control and two groups of treatment. A single dose of 15 Gy radiation was provided to the ventral region of the neck in all treatment groups, damaging the salivary glands. BM-MSCs transplantation was performed in the treatment groups for normoxia and hypoxia 24-hour postradiation. STATISTICAL ANALYSIS: Statistical analysis was done using normality test, followed by MANOVA test (p < 0.05). RESULTS: There was a significant difference in the expression of binding SDF1-CXCR4, Bcl-2 (p < 0.05) and also the activity of the enzyme α-amylase in all groups of hypoxia. CONCLUSION: BM-MSCs transplantation with hypoxic precondition increases the expression of binding SDF1-CXCR4, Bcl-2 that contributes to cell migration, cell survival, and cell differentiation.

Hypoxic preconditioning effect on stromal cells derived factor-1 and C-X-C chemokine receptor type 4 expression in Wistar rat's (Rattus norvegicus) bone marrow mesenchymal stem cells (in vitro study).

AIM: To examine the effect of hypoxic preconditions on the ability of bone marrow stem cells culture mediated expression C-X-C chemokine receptor type 4 (CXCR4) and stromal cells derived factor-1 (SDF-1) in vitro. MATERIALS AND METHODS: Bone marrow mesenchymal stem cells (BMSCs) were derived from 12 femurs of 200 g Wistar male rats. The animals were euthanized before BMSCs isolation. BMSCs were divided into two groups, control group: Normoxic condition 21% O2 and treatment group: Hypoxic condition 1% O2. The characterization of BMSCs was analyzed using flow cytometry by cluster differentiation 34 and cluster differentiation 105. The expression of CXCR4 and SDF-1 measured using immunocytochemistry immunofluorescence label after 48-h incubation in a low-tension oxygen chamber with an internal atmosphere consisting of 95% N2, 5% CO2, and 1% O2. All data were subjected to a normality test and then analyzed using t-test statistic (p<0.05). RESULTS: The characterization of bone marrow stem cells showed positive cluster differentiation 34 and cluster differentiation 105. A hypoxic precondition (1% O2) in culture increases CXCR4 (p=0.000) and SDF-1 expression than normoxic conditions (p=0.000) (p<0.05). CONCLUSION: Hypoxic preconditioning with 1% O2 increase CXCR4 and SDF1 expression.