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.

Temporal Pattern of micro-CT Angiography Vascular Parameters and VEGF mRNA Expression in Fracture Healing: a Radiograph and Molecular Comparison.

Angiogenesis plays an important role in fracture healing with vascular endothelial growth factor (VEGF) as the main protein involved. Micro-computed tomography (CT) angiography may be used to analyze this revascularization with several parameters such as number of branches, total volume, and diameter. This systematic review is aimed to assess available studies on the temporal pattern of vascular imaging on micro-CT angiographs, especially in terms of the number of branches, total volume, and diameter as well as the temporal pattern of VEGF mRNA expression as the molecular comparison during bone fracture healing. This review was conducted according to the PRISMA guidelines. Electronic database searches were performed using PubMed, ProQuest, ScienceDirect, EBSCOhost, Taylor & Francis Online, and hand searching. The search strategy and keywords were adjusted to each database using the Boolean operators and other available limit functions to identify most relevant articles based on our inclusion and exclusion criteria. Screening and filtration were done in several stages by removing the duplicates and analyzing each title, abstract, and full-text in all included entries. Data extraction was done for syntheses to summarize the temporal pattern of each parameter. A total of 28 articles were eligible and met all criteria, 11 articles were synthesized in its angiograph's analysis, 16 articles were synthesized in its VEGF mRNA expression analysis, and 1 article had both parameters analyzed. The overall temporal pattern of both three micro-CT angiographic parameters and VEGF mRNA expression was in line qualitatively. The number of branches, total volume, and diameter of the blood vessels in micro-CT angiography showed an exponential rise at week 2 and decline at week 3 of fracture healing, with the VEGF mRNA expression concurrently showing a consistent pattern in the phase.

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.