With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising results for the use of machine learning in implant brand detection. However, despite these promising findings, a comprehensive evaluation of the accuracy of machine learning in implant brand detection is needed. The purpose of this systematic review and meta-analysis was to assess the accuracy, sensitivity, and specificity of deep learning algorithms in implant brand detection using 2-dimensional images such as from periapical or panoramic radiographs. Electronic searches were conducted in PubMed, Embase, Scopus, Scopus Secondary, and Web of Science databases. Studies that met the inclusion criteria were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analyses were performed using the random-effects model to estimate the pooled performance measures and the 95% confidence intervals (CIs) using STATA v.17. Thirteen studies were selected for the systematic review, and 3 were used in the meta-analysis. The meta-analysis of the studies found that the overall accuracy of CNN algorithms in detecting dental implants in radiographic images was 95.63%, with a sensitivity of 94.55% and a specificity of 97.91%. The highest reported accuracy was 99.08% for CNN Multitask ResNet152 algorithm, and sensitivity and specificity were 100.00% and 98.70% respectively for the deep CNN (Neuro-T version 2.0.1) algorithm with the Straumann SLActive BLT implant brand. All studies had a low risk of bias. The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms.

The Coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health crisis with unmatched outcomes and effects. This pandemic has caused an infodemic of article publication in scientific journals. Dental journals have been active in the publication of COVID-19 related articles from the beginning of the pandemic. In this cross-sectional survey, we present an analysis of the scientific output of dental journals on COVID-19. The PubMed COVID-19 database was searched with the “Dental Journals” filter. Data including journal name, country, month of publication and number of citations were recorded. Science mapping analysis of the most used keywords was also performed. The search retrieved a total of 659 articles, of which 28 were excluded. Oral Diseases has published the most COVID-19 articles (15.1%), followed by the British Journal of Oral and Maxillofacial Surgery (8.6%) and the Journal of Dental Education (7.9%). Most of the articles were from researchers from the United States (168), United Kingdom (120) and Brazil (83). The number of citations of the published articles ranged from 0 to 406, with most articles (64.2%) having no citations. Science Mapping analysis revealed that the most used keywords were coronavirus infections, pandemics and humans. The dental community has been active in the publication of COVID-19 articles from the beginning of the pandemic. The papers published by dental journals explore issues such as the management of clinical practices during the outbreak, infection control in the dental setting, signs and symptoms of COVID-19 affecting the oral cavity, and the impact of the COVID-19 pandemic on educational and clinical programs.

Background: The routine application of dental implants for replacing missing teeth has revolutionized restorative and prosthetic dentistry. However, cone beam computed tomography (CBCT) evaluations of structures adjacent to the implants are limited by metal artifacts. There are several methods for reducing metal artifacts, but this remains a challenging task. This study aimed to examine the effectiveness of metal artifact reduction (MAR) algorithms in identifying injuries of implants to the inferior alveolar canal in CBCT images. Method: In this in vitro study, mono-cortical bone windows were created and the inferior alveolar canal was revealed. Using 36 implants, pilot drill and penetration damage of the implant tip into the canal was simulated and compared to the control implants with distance from the canal. CBCT images were evaluated by four experienced observers with and without the MAR algorithm and compared to direct vision as the gold standard. The values of accuracy, sensitivity, and specificity were obtained and compared by receiver operating characteristic (ROC) curve (α = 0.05). Result: The area under the ROC curve values for detection of pilot drill injuries varied between 0.840–0.917 and 0.639–0.854 in the active and inactive MAR conditions, respectively. The increase in ROC area was only significant for one of the observers (P = 0.010). For diagnosing penetrative injuries, the area under the ROC curve values was between 0.990–1.000 and 0.722–1.000 in the active and inactive MAR conditions, respectively. The improvement of ROC curve values in active MAR mode was only significant for one of the observers (P = 0.006). Conclusion: Activation of MAR improved the diagnostic values of CBCT images in detecting both types of implant-related injuries to the inferior alveolar canal. However, for most observers, this increase was not statistically significant.

Detecting the second mesiobuccal (MB2) canal in maxillary first molars is challenging, even with cone-beam computed tomography (CBCT). Artificial intellige

(1) Background: Fractal analysis has been used as a mathematical method for studying the complexity of fractal structures such as trabecular bone that look similar at different scales. Bruxism is a disorder involving nonfunctional grinding and clenching of the teeth that leads to bone resorption and fractal dimension reduction. This study aimed to evaluate the trabecular pattern of the mandibular condyle, angle, and dental region in panoramic radiographs of individuals with and without bruxism using fractal analysis and a larger sample size. (2) Methods: A total of 365 panoramic radiographs belonging to two groups consisting of bruxism and non-bruxism individuals were assessed using fractal analysis. Fractal dimension (FD) values were calculated on each side for the three regions of interest (ROIs): the mandibular condylar head, mandibular angle, and interdental region between the second premolar and first molar. Statistical analysis was performed using binominal and chi-square tests, the Shapiro–Wilk test, and the Mann–Whitney test (α = 0.05). (3) Results: No significant differences were observed between the FD values of the ROIs in the two groups (p > 0.05). (4) Conclusions: No significant differences existed in the FD values of the ROIs in patients with and without bruxism. This result shows that fractal analysis of panoramic radiographs cannot be useful in detecting patients with bruxism.