COVID-19 pandemic led to major life changes. We assessed the psychological impact of COVID-19 on dental academics globally and on changes in their behaviors. We invited dental academics to complete a cross-sectional, online survey from March to May 2020. The survey was based on the Theory of Planned Behavior (TPB). The survey collected data on participants’ stress levels (using the Impact of Event Scale), attitude (fears, and worries because of COVID-19 extracted by Principal Component Analysis (PCA), perceived control (resulting from training on public health emergencies), norms (country-level COVID-19 fatality rate), and personal and professional backgrounds. We used multilevel regression models to assess the association between the study outcome variables (frequent handwashing and avoidance of crowded places) and explanatory variables (stress, attitude, perceived control and norms). 1862 academics from 28 countries participated in the survey (response rate = 11.3%). Of those, 53.4% were female, 32.9% were <46 years old and 9.9% had severe stress. PCA extracted three main factors: fear of infection, worries because of professional responsibilities, and worries because of restricted mobility. These factors had significant dose-dependent association with stress and were significantly associated with more frequent handwashing by dental academics (B = 0.56, 0.33, and 0.34) and avoiding crowded places (B = 0.55, 0.30, and 0.28). Low country fatality rates were significantly associated with more handwashing (B = -2.82) and avoiding crowded places (B = -6.61). Training on public health emergencies was not significantly associated with behavior change (B = -0.01 and -0.11). COVID-19 had a considerable psychological impact on dental academics. There was a direct, dose-dependent association between change in behaviors and worries but no association between these changes and training on public health emergencies. More change in behaviors was associated with lower country COVID-19 fatality rates. Fears and stresses were associated with greater adoption of preventive measures against the pandemic.

The accuracy of artificial intelligence-aided (AI) caries diagnosis can vary considerably depending on numerous factors. This review aimed to assess the diagnostic accuracy of AI models for caries detection and classification on bitewing radiographs. Publications after 2010 were screened in five databases. A customized risk of bias (RoB) assessment tool was developed and applied to the 14 articles that met the inclusion criteria out of 935 references. Dataset sizes ranged from 112 to 3686 radiographs. While 86 % of the studies reported a model with an accuracy of ≥80 %, most exhibited unclear or high risk of bias. Three studies compared the model’s diagnostic performance to dentists, in which the models consistently showed higher average sensitivity. Five studies were included in a bivariate diagnostic random-effects meta-analysis for overall caries detection. The diagnostic odds ratio was 55.8 (95 % CI= 28.8 – 108.3), and the summary sensitivity and specificity were 0.87 (0.76 – 0.94) and 0.89 (0.75 – 0.960), respectively. Independent meta-analyses for dentin and enamel caries detection were conducted and showed sensitivities of 0.84 (0.80 – 0.87) and 0.71 (0.66 – 0.75), respectively. Despite the promising diagnostic performance of AI models, the lack of high-quality, adequately reported, and externally validated studies highlight current challenges and future research needs.

Background: COVID-19 is a global pandemic affecting all aspects of life in all countries. We assessed COVID-19 knowledge and associated factors among dental academics in 26 countries. Methods: We invited dental academics to participate in a cross-sectional, multi-country, online survey from March to April 2020. The survey collected data on knowledge of COVID-19 regarding the mode of transmission, symptoms, diagnosis, treatment, protection, and dental treatment precautions as well as participants’ background variables. Multilevel linear models were used to assess the association between dental academics’ knowledge of COVID-19 and individual level (personal and professional) and country-level (number of COVID-19 cases/ million population) factors accounting for random variation among countries. Results: Two thousand forty-five academics participated in the survey (response rate 14.3%, with 54.7% female and 67% younger than 46 years of age). The mean (SD) knowledge percent score was 73.2 (11.2) %, and the score of knowledge of symptoms was significantly lower than the score of knowledge of diagnostic methods (53.1 and 85.4%, P < 0.0001). Knowledge score was significantly higher among those living with a partner/spouse than among those living alone (regression coefficient (B) = 0.48); higher among those with PhD degrees than among those with Bachelor of Dental Science degrees (B = 0.48); higher among those seeing 21 to 30 patients daily than among those seeing no patients (B = 0.65); and higher among those from countries with a higher number of COVID-19 cases/million population (B = 0.0007). Conclusions: Dental academics had poorer knowledge of COVID-19 symptoms than of COVID-19 diagnostic methods. Living arrangements, academic degrees, patient load, and magnitude of the epidemic in the country were associated with COVD-19 knowledge among dental academics. Training of dental academics on COVID-19 can be designed using these findings to recruit those with the greatest need.

Background: The use of silver diamine fluoride (SDF) in caries treatment in children has increased despite the disadvantage of causing tooth discoloration. Nanosilver fluoride (NSF) is a possible alternative. This study aimed to assess the antibacterial effect of NSF and SDF and their impact on the activity of dentin caries in primary teeth. Methods: Synthesis and characterization of the physical and biological properties of NSF were conducted. Fifty children aged 4–6 years with dentin caries (active caries corresponding to ICDAS code 5) in deciduous teeth were randomly assigned to treatment by NSF or SDF. Baseline assessment of Streptococcus mutans (S. mutans) and lactobacilli counts as CFU/mL in caries lesions was done, followed by the application of the agents. After one month, microbiological samples were recollected, and lesion activity was reassessed. Groups were compared using Mann–Whitney and Chi-Square tests, while intragroup comparisons were done using Wilcoxon and McNemar tests. Multilevel logistic regression analysis was used to assess the effect of different variables on the outcomes. Results: There were 130 teeth in 50 children; mean ± SD age = 4.75 ± 0.76 years, 63% were posterior teeth. At the one-month follow-up appointment, both groups showed a significant decrease from baseline bacterial counts. There was a significant difference in the reduction of S. mutans between NSF and SDF (21.3% and 10.5%, respectively, p = 0.002), while not in lactobacilli (13.9% and 6.0%, respectively, p = 0.094). In both groups, there was a significant reduction in the number of active caries from baseline (p < 0.0001) with no significant difference between groups (percentage inactive = 64.4% and 63.4%, p = 0.903). Multilevel regression revealed non-significant differences in S. mutans and lactobacilli counts (AOR 1.281, p = 0.737 and 1.888, p = 0.341, respectively), and in the number of inactive lesions (AOR 1.355, p = 0.731) between groups. Conclusion: The short-term antibacterial efficacy of NSF was similar to that of SDF. In both groups there was a significant reduction of S. mutans and lactobacilli counts in active dentin caries, and two-thirds of the lesions became inactive with no differences between the two interventions. Further research is needed to investigate the long-term efficacy of NSF and its suitability for clinical use in caries management. Trial registration: This trial was prospectively registered on the clinicaltrials.gov registry with ID: NCT05221749 on 03/02/2022.

Background/Objectives: Early childhood caries (ECC) is a widespread and severe oral health problem that potentially affects the general health of children. Visual–tactile examination remains the diagnostic method of choice to diagnose ECC, although visual examination could be automated by artificial intelligence (AI) tools in the future. The aim of this study was the external validation of a recently published and freely accessible AI-based model for detecting ECC and classifying carious lesions in dental photographs. Methods: A total of 143 anonymised photographs of anterior deciduous teeth (ECC = 107, controls = 36) were visually evaluated by the dental study group (reference test) and analysed using the AI-based model (test method). Diagnostic performance was determined statistically. Results: ECC detection accuracy was 97.2%. Diagnostic performance varied between carious lesion classes (noncavitated lesions, greyish translucency/microcavity, cavitation, destructed tooth), with accuracies ranging from 88.9% to 98.1%, sensitivities ranging from 68.8% to 98.5% and specificities ranging from 86.1% to 99.4%. The area under the curve ranged from 0.834 to 0.964. Conclusions: The performance of the AI-based model is similar to that reported for the internal dataset used by developers. Further studies with independent image samples are required to comprehensively gauge the performance of the model.