International dental journal最新文献

Background: Mandibular distraction osteogenesis (MDO) is critical for correcting mandibular hypoplasia. Mixed reality (MR) is an emerging surgical-navigation technology that can overcome the limitations of traditional methods. However, evidence regarding the benefits of MR navigation in MDO is limited. Herein, we developed a dynamic MR-based navigation system and compared its performance to techniques using surgical guides (SGs) as well as freehand (FH) and traditional navigation (TN) approaches.

Methods: An adjacent-display MR navigation system integrating optical tracking and a patient-specific registration device was developed for real-time dynamic guidance. Thirty mandibular three-dimensional (3D)-printed models were allocated to three groups (n = 10 each): MR, SG, and FH. Thirty-two Beagle dogs were randomised into four groups (n = 8 each): MR, SG, FH, and TN. The feasibility of the MR system was evaluated in 10 human cadaver heads. All procedures were performed by the same surgical team. The outcomes included osteotomy accuracy, distraction-vector deviation, distractor placement precision, intraoperative corrections, and operative time.

Results: In the model experiments, the MR group showed significantly higher distractor and osteotomy accuracy than the FH group and similar precision as the SG group. In animal studies, the MR approach reduced errors in distractor placement, distraction vector, and osteotomy in comparison with the FH approach, and outperformed the SG-based approach in terms of distractor positioning and osteotomy accuracy. In comparison with TN, the MR approach excelled in distractor positioning, osteotomy accuracy, and reduced operative time. Cadaver experiments revealed sagittal and transverse distractor errors of 1.99° ± 0.67° and 1.63° ± 0.88°, respectively, distraction-vector deviation of 2.62° ± 0.38°, and osteotomy angle deviation of 1.74° ± 0.50°. The average operative time was 85.23 ± 13.04 min; distractor positioning required 54.08 ± 15.71 min and the number of intraoperative corrections was 6.80 ± 1.87.

Conclusions: The dynamic MR navigation system achieved better accuracy than the FH approach and showed specific advantages over the TN and SG-based approaches, indicating its potential clinical applicability for precise MDO.

Trial registration: This study was registered with the Chinese Clinical Trial Registry (ChiCTR2500098863).

Objectives: Age-related differences in orthodontic tooth movement (OTM) and mechanical force-induced osteogenesis have been reported. Mitophagy plays a crucial role in bone metabolism and various age-related diseases, and BCL2-interacting protein 3 (BNIP3) is a mitophagy-related receptor. This study aimed to elucidate the role of mitophagy associated with BNIP3 on age-related changes in the orthodontic tension-driven osteogenic differentiation of periodontal ligament stem cells.

Materials and methods: Periodontal ligament stem cells (PDLSCs) from adolescent (6-week-old) and adult (8-month-old) rats were cultured and stretched using a Flexcell system. The effects of mitophagy associated with BNIP3 were assessed via real-time quantitative PCR and western blot analyses. Moreover, a rat model of OTM across different ages was established for in vivo analyses. The function of mitophagy in age-related osteogenic differentiation induced by orthodontic force on the tension side was evaluated via microcomputed tomography and immunohistochemistry analyses.

Results: Under tension, the expression of the mitophagy factor BNIP3, the autophagy factor microtubule-associated protein light chain 3 (LC3), and the osteogenic factors Runt-related transcription factor 2 (RUNX2) and Osterix (OSX) significantly increased in rPDLSCs over time. The expression of these factors was also upregulated in the rat OTM model under orthodontic force. Compared with the adolescent group, the adult group exhibited lower levels of mitophagy and osteogenic differentiation after tension both in vivo and in vitro. Enhanced mitophagy induced by carbonyl cyanide m-chlorophenyl hydrazone upregulated the expression of the aforementioned factors in an adult rat OTM model.

Conclusions: Mitophagy is associated with osteogenic activity induced by tension force in PDLSCs and may play a substantial role in regulating age-related changes in the OTM process.

Background: The surgical extraction of impacted mandibular third molars present significant clinical challenges, where accurate preoperative assessment is crucial to mitigate risks such as Inferior Alveolar Nerve injury. Although artificial intelligence shows promise in dental radiology, existing approaches are often limited to binary classification, affected by class imbalance, and lack standardized evaluation protocols, thereby restricting their clinical applicability.

Methods: This study proposes two independent deep learning frameworks for comprehensive analysis of third molar impactions. The first framework is an end-to-end object detection pipeline employing modified YOLOv10 and YOLOv11n architectures enhanced with multihead self-attention. The second framework is a feature-based classification approach, where deep features extracted using ResNet50 and InceptionNetV3 are classified using traditional machine learning algorithms.

Results: Validated on a multinational dataset of 5796 expertly annotated orthopantomograms with high inter-rater agreement (κ = 0.92), the proposed frameworks demonstrated competitive performance. The Fine KNN classifier using ResNet50 features achieved the best classification performance, yielding 97.56% accuracy, 96.07% precision, 96.21% recall, and an F1-score of 96.10%, while InceptionNetV3-based classification achieved 97.33% accuracy with an F1-score of 95.30%. For object detection, YOLOv11n attained a mean average precision of 88.9% (mAP@0.5) and 85.7% (mAP@0.5:0.95), while maintaining substantially lower computational complexity (19.7 vs 28.4 GFLOPs). Ablation experiments confirmed that the integration of multihead self-attention modules and generative adversarial network-based augmentation improved detection performance by 6.4% mean average precision.

Conclusions: The proposed frameworks enable accurate and automated multiclass assessment of third molar impactions, achieving high diagnostic performance while preserving computational efficiency suitable for clinical deployment. This work advances artificial intelligence-assisted surgical planning by providing reliable F1-score-based evaluation, reliable real-time detection, and enhanced preoperative risk stratification in oral and maxillofacial surgery.

Carotid artery calcifications (CACs), a known risk factor for stroke, can be detected on panoramic radiographs (PRs). However, this clinically significant pathophysiological condition has long been underdiagnosed due to insufficient training and expertise among dentists. Artificial intelligence (AI) may serve as a valuable tool to aid dentists in detecting CACs on PRs. This meta-analysis was conducted to assess the diagnostic accuracy of AI for CACs detection on PRs. A literature search was conducted on PubMed, Embase, Web of Science, Scopus and Cochrane Library up to 4 September 2025 without language limitation. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. The performance of AI was assessed via the area under curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Meta-analysis was conducted on Stata 14.0. This systematic review identified 8 relevant articles, 7 of which were eligible for meta-analysis. The analysis was conducted from two perspectives: per-side (evaluating the left and right sides of participants separately) and per-person. In the per-side analysis, the summary estimates indicated high diagnostic accuracy: sensitivity was 0.88 (95% CI: 0.84-0.92), specificity was 0.94 (95% CI: 0.91-0.97), the PLR was 15.8 (95% CI: 9.0-27.6), the NLR was 0.12 (95% CI: 0.08-0.18), the DOR was 129 (95% CI: 55-305), and AUC was 0.96. The per-person analysis yielded a pooled sensitivity of 0.90 (95% CI: 0.74-0.97) and specificity of 0.81 (95% CI: 0.73-0.87). The corresponding PLR was 4.6 (95% CI: 3.1-7.0), NLR was 0.12 (95% CI: 0.04-0.36), DOR was 37 (95% CI: 10-144), and the AUC was 0.86. These results indicate that AI may serve as a valuable tool to assist dentists in detecting CACs on PRs. However, large-scale, evidence-based studies are still needed to validate these findings.

Background: Delays in dental care worsen oral disease and mirror broader inequities in health care access and use.

Objective: To estimate the 12-month prevalence of delayed dental care among US adults, to characterise disparities across demographic and socioeconomic groups and to evaluate the utility of machine learning models for identifying individuals at elevated risk of delay.

Methods: This study used cross-sectional analysis of the 2023 National Health Interview Survey (NHIS) Sample Adult file (N = 54,927). Survey weights were applied to obtain nationally representative estimates. Descriptive statistics were stratified by age, sex, education, dental insurance coverage and race/ethnicity. In parallel, supervised machine learning classifiers were developed to classify delayed dental care as a binary outcome. Class imbalance was addressed using oversampling applied to training data only. Model performance was evaluated using accuracy, precision, recall, F1-score and area under the receiver operating characteristic curve (AUC), and model interpretability was assessed using SHapley Additive exPlanations (SHAP).

Results: Overall, 14.5% of adults reported delaying dental care in the prior year. Delay was higher among adults aged 35-50 (18.3%) and 51-64 (17.2%) and lower among those 65 or older (11.0%). Racial/ethnic differences were evident: Black/African American adults (19.1%) and individuals reporting multiple races (21.1%) had higher prevalence of delayed care than did White adults (14.2%). Adults without dental insurance were more likely to report delays, underscoring the role of coverage. An educational gradient was observed, with higher delays among those with lower attainment. Among the evaluated classifiers, the Light Gradient Boosting Machine (LightGBM) demonstrated the strongest overall performance (accuracy = 84.87%). The SHAP analysis identified education, income-to-poverty ratio and insurance status as the most influential predictors.

Conclusions: Delayed dental care affects a substantial share of US adults and disproportionately impacts groups defined by age, race/ethnicity, education and insurance status. Interpretable machine learning models can complement traditional survey analyses by supporting population-level risk stratification. Policies that expand dental coverage, reduce financial barriers and target outreach to high-risk populations may mitigate inequities. Given the cross-sectional design, findings should be interpreted as predictive rather than causal. Future research should incorporate contextual determinants (eg, geographic access and provider availability) and longitudinal data to refine population targeting and evaluate intervention impact.

Objectives: To investigate the effect of varying phosphoric acid etching times on dentin demineralisation and collagen fibril morphology and to identify a time-dependent balance between sufficient mineral removal and preservation of collagen nanostructure.

Methods: Dentin specimens from human third molars were etched with 37% phosphoric acid for 6 different durations: 0, 5, 10, 15, 30, and 60 seconds. Demineralisation was assessed using attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy by calculating the phosphate-to-amide I absorbance ratio. Collagen integrity was evaluated using scanning electron microscopy (SEM) and atomic force microscopy (AFM) imaging, with the percentage of visible D-banding serving as a morphometric indicator.

Results: FTIR analysis revealed a progressive reduction in the phosphate signal with increasing etching time, following an exponential decay pattern and approaching a plateau at approximately 10 seconds. AFM imaging revealed that the D-banding periodicity reached its peak at 10 seconds (mean: 71.7%) and subsequently decreased, with notable degradation observed at 30 and 60 seconds. Statistical analysis indicated significant differences in D-banding between the 10-second group and longer etching durations (P < .05).

Clinical significance: This study provides nanoscale evidence that phosphoric acid etching of dentin for approximately 10 seconds achieves effective demineralisation while preserving collagen integrity. Longer etching times compromise fibril structure, underscoring the need for precise, time-controlled protocols to optimise resin-dentin bonding performance.

Objectives: This study aimed to compare and improve the performance of three deep learning models, i.e., U-Net Transformer (UNETR), Swin UNETR, and 3D UX-Net, for the segmentation of the mandibular canal and its branches.

Materials and methods: A dataset of 173 cone beam computed tomography (CBCT) scans was used for training, validation, and testing. The mandibular canals and branches were segmented manually and by the three AI models. A postprocessing module based on anatomical characteristics was then applied to improve model performance. Evaluations were conducted using Dice similarity coefficient (DSC), intersection over union (IoU), 95th Percentile Hausdorff Distance (HD95), average symmetric surface distance (ASSD), precision, and recall.

Results: All models efficiently segmented the mandibular, incisive, and mental canals, operating at least 25 times faster than manual annotation. Both 3D UX-Net and Swin UNETR consistently outperformed the UNETR network across most metrics, with 3D UX-Net demonstrating a slight performance advantage over Swin UNETR in terms of DSC, IoU, and recall. Furthermore, the anatomically-based postprocessing module significantly improved the metrics for all models. Ultimately, the 3D UX-Net with postprocessing achieved the highest accuracy, with mean values of 0.788 (DSC), 0.652 (IoU), 0.23 mm (HD95), 0.083 mm (ASSD), 72.7% (precision), and 87.0% (recall).

Conclusion: 3D UX-Net and Swin UNETR are superior to the UNETR for segmenting small dental structures. Between the two, 3D UX-Net demonstrated statistically significant improvements in overlap and recall. Furthermore, the performances of these models can be significantly enhanced by applying postprocessing strategies based on anatomical characteristics.

Purpose: The aim of this study was to evaluate the clinical, immunological, and microbiological effects of zinc-coated healing abutments (Zn-TiO₂ abutments) on peri-implant soft tissue as compared with the commercially used ones (Ti abutments).

Methods: The study was a prospective, non-randomised, single-centre, open-label, proof-of-concept clinical trial. The Ti and Zn-TiO₂ abutments were non-randomly connected to 2 neighbouring implants in the posterior region in each eligible patient. The bleeding-on-probing proportion (BOP%), probing pocket depth (PPD), the concentration of the pro-inflammatory cytokine (TNF-α, IL-6) in the peri-implant crevicular fluid (PICF), and the early microbial communities assessed by 16S rRNA sequencing were recorded.

Results: Eleven patients with 22 implants attended the 8-week examination. The BOP% was significantly lower in the Zn-TiO₂ abutments than that in the Ti abutments (24.23% ± 13.67% versus 42.42% ± 29.22%, P = .019). The concentration of TNF-α in PICF was significantly lower in the Zn-TiO₂ abutments than that in the Ti abutments (22.86 ± 11.21 versus 32.05 ± 16.28, P = .022). No significant differences in PPD and IL-6 were found between the two groups. Based on the microbiome assessments, higher microbial richness and lower presence of Lancefieldella were also observed in the Zn-TiO₂ abutments as compared with the Ti abutments.

Conclusion: Within the limitations of the study, the zinc-coated healing abutments improved early peri-implant soft tissue health clinically and immunologically. However, further studies are still needed to exclude the interference of soft tissue phenotype and confirm the relationship between microbial and clinical findings.