Competitive esports participation involves prolonged screen exposure, irregular dietary habits, and high cognitive demands, which may be associated with oral health–related behaviours and outcomes. Evidence addressing oral health in this emerging population remains limited. This cross-sectional study included 312 competitive esports athletes and 312 age- and socioeconomic status–matched non-gaming controls aged 16–32 years. Participants completed validated questionnaires assessing oral hygiene practices, dietary behaviours, and perceived stress. Clinical oral examinations evaluated dental caries (DMFT), periodontal status (CPI), erosive tooth wear (BEWE), and bruxism-related symptoms. Salivary concentrations of 8-hydroxy-2′-deoxyguanosine, interleukin-6, and tumor necrosis factor-alpha were analyzed in a predefined subgroup (n = 100). Competitive esports athletes demonstrated lower adherence to recommended oral hygiene practices and a higher prevalence of dental caries, periodontal disease, erosive tooth wear, and bruxism-related symptoms compared with controls (p < 0.001). Salivary oxidative stress and inflammatory marker concentrations were also higher among esports athletes. Competitive esports athletes exhibited distinct oral health behaviours, clinical indicators, and salivary biomarker profiles compared with non-gaming peers, highlighting the potential relevance of oral health assessment within health monitoring frameworks for competitive digital sports.
This retrospective cross-sectional study assessed the prevalence of cone-beam computed tomography (CBCT)-detected periapical radiolucencies and their associations with demographic, anatomical, and treatment-related factors in root canal–treated teeth. CBCT scans from 390 patients (226 females and 164 males; mean age, 45.87 ± 14.96 years) comprising 994 root-filled teeth were examined. Periapical status was assessed using the CBCT periapical index (CBCT-PAI), with CBCT-PAI ≥ 3 defined as the primary outcome. Obturation length and density, procedural errors, tooth location, and patient-level characteristics were evaluated. Adjusted associations were estimated using binary logistic generalized estimating equations accounting for clustering of teeth within patients. CBCT-PAI ≥ 3 was identified in 235 teeth (23.6
Artificial intelligence (AI) has rapidly expanded across digital implant dentistry, with applications ranging from diagnosis and treatment planning to surgical execution, prosthetic rehabilitation, outcome prediction, and biomechanical optimization. However, the clinical readiness and translational value of many proposed AI systems remain uncertain. This comprehensive state-of-the-art narrative review aims to provide an integrated overview of AI applications throughout the implant workflow while critically evaluating current evidence, methodological limitations, and challenges to clinical implementation. A comprehensive narrative literature search was conducted using PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library, supplemented by Google Scholar and manual reference screening. The review was prepared according to SANRA recommendations. AI has demonstrated promising performance in automated image analysis, implant planning, computer-assisted surgery, prosthetic workflows, implant identification, peri-implant disease detection, predictive modelling, and biomechanical optimization. Nevertheless, most available evidence is derived from retrospective studies, simulation-based investigations, or single-center datasets. Limited external validation, heterogeneity in study design, and insufficient prospective clinical evidence continue to restrict widespread clinical adoption. Furthermore, improvements in technical performance do not necessarily translate into superior patient-centered outcomes. AI has the potential to enhance efficiency, consistency, and clinical decision support throughout the implant workflow. However, current evidence supports its role primarily as an adjunctive tool rather than an autonomous clinical system. Future progress will depend on robust validation, standardized reporting, explainable AI frameworks, and prospective clinical studies demonstrating meaningful improvements in patient outcomes. Clinical Relevance Statement: Artificial intelligence is increasingly embedded in the digital implant workflow, from radiographic analysis and virtual patient construction to computer-assisted surgery, prosthetic design, and outcome prediction. This review helps clinicians distinguish the AI-specific components of these technologies from conventional digital tools, identify which applications are supported by clinical evidence versus those that remain experimental, and understand the current limitations regarding validation, generalizability, and regulatory status. This knowledge supports informed adoption of AI as an adjunctive decision-support tool while maintaining clinician-led verification and patient-centered care.
This prospective clinical study investigated time-dependent changes in salivary bisphenol A (BPA) and bisphenol S (BPS) concentrations during clear aligner therapy. 20 participants were treated with Invisalign® clear aligners. Stimulated saliva samples were collected before aligner insertion at baseline (Day 0) and on Days 3, 7, 14, and 21. BPA and BPS concentrations were measured using commercially available enzyme-linked immunosorbent assay kits. Changes across time points were assessed using the Friedman test, followed by Bonferroni-adjusted Wilcoxon signed-rank tests for pairwise comparisons. BPA concentrations changed significantly over time (p = 0.0021). The highest mean BPA concentration was observed on Day 3 (4.85 ± 1.89 ppb), and the Day 3 concentration was significantly higher than those measured on Days 14 and 21. BPS concentrations also changed significantly over time (p = 0.0063), with the lowest mean concentration observed on Day 14 (2.79 ± 0.10 ppb). This value was significantly lower than those measured at baseline and on Days 3, 7, and 21. These findings indicate time-dependent changes in salivary BPA and BPS concentrations during clear aligner therapy. The observed temporal patterns do not establish material-specific release or systemic exposure.
The genetic basis of susceptibility to early childhood caries (ECC) requires further research. To examine the possible association between the miR-196a2 rs11614913 polymorphism and susceptibility to ECC. This case-control study recruited 200 children with ECC and 200 non-ECC children. Genotyping of the rs11614913 polymorphism was performed by the TaqMan method, and the levels of miR-196a2 in saliva were quantified by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Logistic regression models were employed to analyse risk factors associated with ECC. Spearman’s correlation analysis was conducted to assess the relationship between miR-196a2 and the caries severity index. T allele and TT genotype were associated with a substantially increased risk of ECC (odds ratio (OR) = 1.697, 95
