Journal of Dental Research

Background Alveolar bone assessment in periodontal practice relies on radiography and clinical probing, both of which have well-documented limitations in precision. Intraoral high-frequency ultrasonography (US) offers a radiation-free alternative with potential for sub-millimeter resolution, the validity and precision for detecting minute osseous changes have not been established. The purpose of this study was to evaluate the concurrent validity and measurement precision of intraoral US for detecting alveolar bone-level changes in patients undergoing crown lengthening and osseous surgery, thereby enabling its translation to monitor osseous changes in patients with periodontitis. Methods Ten patients (28 tooth sites) undergoing crown lengthening or osseous surgery at a USC Advanced Grad Perio clinic were enrolled in this prospective observational study. Distance from the cementoenamel junction (CEJ) to the Alveolar bone crest (ABC) was measured at pre- and post-operative time points using a 40 MHz handheld intraoral US transducer and, intraoperatively, by standardized clinical photography. Agreement was assessed by Pearson correlation and Bland-Altman analysis. Measurement precision was quantified using the standard error of measurement (SEM) and minimum detectable change (MDC). Results Preoperative agreement between methods was excellent (r = 0.977; Bland-Altman bias = -0.009 mm; 95% limits of agreement [LoA]: +-0.40 mm). Post-operative correlation remained strong (r = 0.912; bias = 0.123 mm; LoA: -0.85 to +1.10 mm). Both methods detected statistically significant post-surgical increases in the ABC-to-CEJ distance (p < 0.001), as anticipated. US demonstrated substantially superior precision: preoperative SEM 0.058 mm with US versus 0.128 mm clinically, yielding MDC values of 0.160 mm (US) versus 0.354 mm (clinical), providing a 2.2-fold precision advantage. Conclusions Intraoral US demonstrated strong concurrent validity with clinical photography and a reproducible precision advantage in detecting alveolar bone-level changes in patients with periodontitis. These findings support its clinical utility as a radiation-free, high-sensitivity bone monitoring tool. Larger longitudinal studies with CBCT validation are warranted.

We conducted a multi-ancestry genome-wide association study (GWAS) of developmental defects of enamel (DDE) in the primary dentition among 6,061 U.S. preschool-aged children (3--5 years). We investigated four DDE phenotypes (demarcated opacities, diffuse opacities, hypoplastic defects, and a combined DDE trait) leveraging main-effect models, joint gene-sex interaction testing (2df), and sex-stratified analyses. SNP-based heritability for the combined DDE trait was estimated at 20%, with concordance analyses robustly supporting a genetic etiology. We identified 39 unique genome-wide significant loci (P<5 x 10-8;), with five surpassing a study-wide Bonferroni-corrected statistical significance criterion (P<1.25 x 10-9), including Y RNA and ALDH1A1. The main-effect GWAS identified 20 loci, including HBS1L and MYB, genes regulating hematopoiesis with plausible roles in amelogenesis. Joint test and sex-stratified analyses revealed 19 additional loci, including ALDH1A1, TENM2, and DLGAP2, demonstrating sex-specific heterogeneity. Nineteen loci exhibited sex-specific differences after Bonferroni correction (P<2 x 10-3), including genes involved in retinoic acid signaling (ALDH1A1), odontogenesis (TENM2), and neurodevelopment (DLGAP2, CDH10). Pathway enrichment highlighted ectodermal and synapse organization networks, suggesting shared etiological mechanisms between DDE and systemic conditions like neurofibromatosis and autism spectrum disorder. Notably, no locus generalized in an external GWAS of permanent dentition DDE, underscoring fundamental biological differences in the genetic architectures governing primary versus permanent enamel formation. Crucially, a comprehensive cross-trait pleiotropy lookup against early childhood caries (ECC) revealed no shared genetic architecture, supporting the notion that the established clinical and epidemiological association between DDE and ECC is likely driven by structural defects increasing caries lesion susceptibility rather than genetic pleiotropy. By integrating gene-sex interaction testing, this study offers novel insights into the complex, sexually dimorphic genetic etiology of DDE and augments the biological evidence base that can support the development of precision pediatric dentistry.

Objective. A deep learning (DL) model was used to convert smartphone videos of a complete arch implant cast into 3D scans. The aim of current study was to determine if a custom scan body (SB) with geometric features and coating would outperform regular PEEK stock SB in this DL scenario. The DL-derived scan outcomes were compared with those obtained from a conventional splinted open-tray impression and from photogrammetry. Materials and Methods. A maxillary edentulous model with six implants and multi-unit abutment analogs was scanned using four protocols: conventional splinted open-tray impression (CO), photogrammetry (PG; Icam4D), DL using stock SBs (DLS) and DL using custom SBs (DLC). Each protocol was repeated for 10 times. The DL scans were produced from smartphone videos with a high-fidelity, multi-view 3D construction AI model (Neuralangelo). The custom designed SB incorporated geometric features and was fabricated via 3D printing followed by a spray coating. Accuracy (trueness and precision) was assessed using three measurements: Root Mean Square (RMS), linear deviation, and angular deviation. Results. DLC outperformed DLS in both trueness and precision regarding RMS and linear measurements (p<0.001). CO and PG demonstrated the highest RMS and linear trueness, with no significant difference between them (RMS: p=0.93; linear: p=0.663). PG achieved the best precision across RMS, linear and angular measurements. Conclusion. The optimised SB significantly improves the accuracy of DL-based approach for full-arch implant scan comparing to regular PEEK stock scan bodies. While early stage, neural surface reconstruction has potential as a viable option for full-arch implant rehabilitation.

Abstract Aim: To test whether neighbouring-tooth restorations affect a focal tooths periodontal status (within-mouth spillover), and whether accounting for them alters the established association at the restored surface. Materials and methods: Prospective tooth-surface data from the Study of Health in Pomerania (SHIP): SHIP-START (N=2,715/1,982/1,397, follow-ups 1-3) and SHIP-TREND (N=2,123). Own and neighbouring-tooth restoration were defined at baseline; probing depth (PD), clinical attachment level (CAL), bleeding on probing (BOP) and deep pockets (PD >=4 mm) at follow-up. A neighbourhood-exposure mixed model with generalized propensity-score adjustment estimated both effects, with two corroborating estimators. Results: Own restoration was associated with worse periodontal status at that surface (crown PD exp(beta) up to 1.10). Neighbouring-tooth restorations raised focal-tooth PD (spillover exp(beta) 1.03-1.05) and deep-pocket risk (risk ratio up to 1.25); CAL and BOP showed none consistently. It attenuated with tooth-lag, nearing the null by two to three teeth in most estimators. Ignoring interference modestly inflated the direct effect; an opposing-tooth negative control showed none; untreated caries reproduced it. Conclusions: Dental restorations exert a within-mouth spillover on neighbouring-tooth PD: their periodontal footprint extends beyond the restored tooth. The direct association persists, modestly attenuated, after accounting for the neighbourhood.

Background: Subepithelial connective tissue grafting remains a reference treatment for predictable root coverage. Although short- and medium-term outcomes of tunnel-based procedures are well documented, evidence regarding stability beyond 10 years remains limited. This study evaluated the long-term clinical performance of a minimally invasive tunnel technique combined with subepithelial connective tissue grafting (SCTG) under routine clinical conditions. Methods: This retrospective longitudinal cohort study included 74 patients (57 women and 17 men) contributing 710 gingival recession sites treated between 2009 and 2025. All sites were treated with a tunnel approach and SCTG, with enamel matrix derivative (EMD) used in selected cases. The mean follow-up was 6.0 for 4.0 years, with a maximum observation period of 16 years. The primary outcome was recession depth reduction. Secondary outcomes included complete root coverage (CRC), mean root coverage, and long-term marginal stability. Clinically relevant relapse was defined as a 1 mm increase in recession after initial healing. Results: Mean recession reduction was 2.72 mm. Complete root coverage was achieved at 83.4% of treated sites. At the final available follow-up, no treated site showed a clinically relevant relapse of 1 mm after initial healing, and no site deteriorated beyond its baseline recession level. Treatment effects were observed across anterior and posterior regions. Conclusions: Within the limitations of a retrospective cohort design, tunnel surgery combined with SCTG was associated with high root-coverage predictability and durable marginal soft-tissue stability for observation periods extending to 16 years. These real-world data support phenotype-enhancing, minimally invasive soft-tissue augmentation as a durable therapeutic strategy for localized and multiple gingival recessions. Keywords: gingival recession; tunnel technique; subepithelial connective tissue graft; root coverage; periodontal plastic surgery; long-term stability

Background: Accurately identifying patients who will require opioids after third molar extraction could improve pain management while supporting opioid stewardship. This study evaluated surgeon accuracy in predicting supplemental opioid use following treatment with ibuprofen and acetaminophen. Methods: Patients (N=85) undergoing third molar extraction were treated with a standardized analgesic regimen of ibuprofen+acetaminophen, with supplemental opioid if needed. Four surgeons independently reviewed preoperative radiographs, assessed surgical difficulty using the Pederson scale, and rated the likelihood of supplemental opioid use on a 5-point Likert scale. Inter-rater reliability was assessed using intraclass correlation coefficients (ICC). The relationship between surgeon ratings and postoperative opioid use was evaluated using logistic regression and receiver operating characteristic (ROC) analysis. Results: Seventeen patients used supplemental opioid analgesics. Inter-rater reliability among surgeons was moderate (ICC3=0.606, 95%CI: 0.505-0.700), while reliability of the average rating across surgeons was good (ICC3k = 0.860, 95% CI: 0.804-0.903). Median surgeon rating was not associated with postoperative opioid use (OR: 0.800, 95% CI: 0.414-1.51, p=0.496) and demonstrated poor discrimination (AUC: 0.551, 95% CI: 0.392-0.710). Surgeon ratings were positively associated with Pederson score (beta=0.073, 95%CI: 0.050-0.096; p<0.001). Conclusions: Surgeons demonstrated moderate agreement, but these assessments did not accurately identify patients who ultimately required supplemental opioids. Surgeon judgments appeared to be influenced by anticipated surgical difficulty. Practical Implications: Clinicians should follow current recommendations against routine "just-in-case" opioid prescribing after third molar extraction. Future studies should focus on identifying clinical and biological predictors of inadequate analgesic response to NSAIDs to support individualized pain management strategies.

Relapse of aligned mandibular anterior teeth and the progressive collapse of the mandibular anterior arch are historically striking problems for orthodontists. The etiology of this collapse, and the cause of mesial molar drift, are unknown. However, light continuous (quasi-continuous) intra-oral pressures and forces applied to the mandibular dentition have been implicated. To explore this further, we use three-dimensional finite element analysis to investigate the influence of these intra-oral loads (tongue pressure, lip-cheek pressure, and interdental force) on mandibular arch collapse and mesial molar drift. Dentitions of three-dimensional finite element mandibular models were subjected to a wide range of simulated tongue pressures, lip-cheek pressures, and transseptal fiber-mediated interdental forces reported in the literature. Resulting crown displacement measurements from these isolated loads were made along with measurements resulting from simultaneous combined application of literature-defined mean tongue pressure, lip-cheek pressure, and interdental force. Our results indicate that tongue pressure alone results in generalized arch expansion and tooth spacing while lip-cheek pressure and interdental force result in generalized arch collapse, anterior crowding, and mesial molar displacement. Simultaneous application of tongue pressure, lip-cheek pressure, and interdental force mean values, as would occur in vivo, results in incisor crowding, intercanine width reduction, and mesial molar displacement. Our results suggest mandibular anterior arch collapse (incisor crowding / intercanine width reduction), and mesial molar displacement result from simultaneous application of tongue pressure, lip-cheek pressure, and interdental force.

Objective: To examine whether Medicaid-enrolled preschoolers with untreated decayed teeth received dental treatment within one year of enrollment and identify the factors associated with a treatment gap. Methods: A retrospective cohort analysis of data from a cluster-randomized trial conducted in 18 community-based pediatric primary care practices in Northeastern Ohio (2017-2022). Treatment receipt was determined using Medicaid claims, with treatment gap defined as fewer teeth with treatment claims than teeth found on baseline exam with decay. Multivariable logistic regression assessed the association of treatment gap with child age, sex, race/ethnicity, caregiver education, and number and location of baseline decayed teeth. Results: Of 766 eligible children, 487 (63.6%) attended the dentist within one year. Among 155/487 (31.8%) with baseline untreated decay, 90/155 (58.1%) had a treatment gap. Odontograms visually showed that decay was concentrated on upper anterior and posterior teeth. A treatment gap was associated with a greater number of decayed posterior teeth (OR = 1.90, 95% CI: 1.60-2.30) and decayed anterior teeth (OR = 2.19, 95% CI: 1.51-3.39), both p < 0.001. Other socio-demographic variables were not significantly associated with a treatment gap. Conclusion: More than half of Medicaid-enrolled children attending well-child visits had a dental treatment gap after 1 year. This pattern may reflect dentists' hesitancy to restore primary teeth nearing exfoliation and needing multiple dental visits to complete needed restorative treatment. To address this gap, non-surgical interventions such as silver diamine fluoride can be applied by pediatric primary care providers to control the bacteria and prevent disease progression.

This cross-sectional study evaluated the association of tooth loss and oral health-related quality of life (OHRQoL) with sexual function in adult women attending a primary dental care service. Methodology: Ninety-nine sexually active women aged 19-66 years were consecutively recruited from a primary dental care service between January and October 2023. Tooth loss was quantified by standardized oral examination. OHRQoL was assessed using the Oral Health Impact Profile-14 (OHIP-14), and sexual function was assessed using the Female Sexual Function Index (FSFI). Sexual dysfunction was defined as FSFI <=26.5. Spearman rank correlation was used for bivariate analyses. Multivariable logistic regression was used to evaluate factors associated with sexual dysfunction, including number of missing teeth, OHIP-14 score, age, and relationship status. Results: Tooth loss was present in 83.8% of participants, with a median of 4 missing teeth (interquartile range [IQR], 1-10). Sexual dysfunction was identified in 62.6% of women. FSFI scores were negatively correlated with number of missing teeth (rho = -0.407; p < 0.001), OHIP-14 score (rho = -0.279; p = 0.005), and age (rho = -0.334; p < 0.001). In multivariable logistic regression, OHIP-14 score was independently associated with sexual dysfunction (OR = 1.05; 95% CI, 1.01-1.10; p = 0.015), whereas number of missing teeth was not independently associated after adjustment. Conclusion: Worse OHRQoL was independently associated with sexual dysfunction, whereas tooth loss was associated with lower FSFI scores only in bivariate analysis. These findings are compatible with the hypothesis that the impact of tooth loss on sexual function may be partly explained by oral health-related quality of life, but longitudinal studies are required to test causal and mediational pathways. Keywords: tooth loss; oral health; quality of life; sexual dysfunction, physiological; women; cross-sectional studies

Periodontitis, a chronic inflammatory disease affecting approximately 40% of U.S. adults aged 30 years and older, is characterized by dysbiosis of the dental plaque microbiome. However, although scaling and root planing (SRP) is the cornerstone of periodontal treatment, its effects on the taxonomic composition and functional potential of the dental plaque microbiome remain incompletely understood. In this study, we used whole-metagenome shotgun sequencing to characterize taxonomic composition and functional potential in dental plaque microbiomes collected from 39 patients with Stage II or III generalized periodontitis before and 3-4 months after SRP. Consistent with clinical improvement, periodontal therapy significantly reduced bleeding on probing and plaque index. Whole-metagenome shotgun sequencing identified 3.18 million non-redundant genes and 12,353 microbial species across 78 samples, revealing increased gene and species richness after treatment, along with a significant restructuring of microbial community. Established periodontal pathogens, including Porphyromonas gingivalis and Tannerella forsythia, as well as the emerging pathogen Escherichia coli, decreased following treatment, whereas health-associated early colonizers, including multiple Actinomyces species and Streptococcus cristatus, increased. Functional annotation using the Carbohydrate-Active Enzymes (CAZy) database identified treatment-associated differences in several carbohydrate-active enzymes, including multiple glycosyltransferases, indicating remodeling of the predicted functional potential of the dental plaque microbiome. These findings demonstrate that successful SRP promotes coordinated taxonomic and predicted functional remodeling of the dental plaque microbiome and highlight the value of shotgun metagenomic sequencing for characterizing both taxonomic and functional recovery following periodontal therapy.

Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficulty. Methods: We assembled 125 multiple-choice questions on dental antibiotic prescribing from eight peer-reviewed studies (2017-2023). GPT-4o answered each question and generated a clinical justification. Accuracy was assessed against source-study answer keys and examined across difficulty quartiles. Justifications were evaluated using an adapted 12-axis human-evaluation framework assessing scientific consensus, extent and likelihood of harm, inappropriate and missing content, bias, and both correct and incorrect comprehension, retrieval, and reasoning. Prophylaxis-specific questions were analysed separately. Results: GPT-4o correctly answered 72% of questions. Accuracy remained relatively stable across difficulty quartiles (78%, 78%, 65%, 70%). Experts rated 95.4% of justifications positively across the 12 axes. Comprehension, retrieval, and reasoning each exceeded 96.2% positive ratings. Missing content was the main weakness (7.8%), and 7.1% of justifications showed a moderate-to-severe potential for harm. Performance on prophylaxis-specific questions (98.1%) exceeded non-prophylaxis questions (93.0%). Conclusions: GPT-4o demonstrated moderate-to-high proficiency and clinically defensible reasoning in dental antibiotic prescribing questions. However, residual risks indicate that it is not suitable for unsupervised clinical use but shows potential as a supervised AMS educational tool.

Since model dose-response is critical when assessing caries lesion development over time, this study evaluated the influence of fluoride dose and treatment duration on caries progression in a rat caries model. Streptococcus mutans-infected Sprague-Dawley rats were treated with deionized water, 226 ppm F-, or 2,260 ppm F- twice daily for 3, 4, or 5 weeks. Caries lesions were assessed using Larson's modification of the Keyes scoring system and complemented by micro-computed tomography (microCT). Intraoral fluoride availability, serum and bone fluoride concentrations and microbial counts were also determined. Fluoride reduced caries severity in a dose- and time-dependent manner. While early enamel lesions were detected in all groups, extensive dentine lesions increased over time, in a dose-dependent manner, in the control and 226 ppm F- groups, and were not observed in the 2,260 ppm F- group after 5 weeks. Intraoral and bone fluoride availability increased significantly with fluoride concentration and treatment duration, whereas serum fluoride levels reflected fluoride dose instead of treatment duration. MicroCT-derived enamel volume correlated negatively with both total and extensive caries scores, supporting its utility as an objective measure of lesion severity. In conclusion, extending model length from 3 to 5 weeks increased the severity of caries lesions in a dose-dependent manner. Fluoride intraoral availability and bone fluoride also demonstrated a dose and time-dependent response.

Background: Cone-beam computed tomography (CBCT) frequently captures the maxillary sinuses incidentally, and reliable automated detection of sinus abnormality is clinically relevant. Unlike most vision-language benchmarks in medical imaging, which pair images with pre-existing, human-authored clinical reports, findings text can also be generated directly by a large language model from the image itself--raising the question of how much diagnostic value such AI-derived text carries, and whether that value depends on independent verification. Multimodal artificial intelligence (AI) benchmarks risk overstating performance if the provenance of each input--image, raw AI-generated text, or radiologist-verified text--is not clearly separated and reported. Methods: We used 300 mid-sagittal CBCT slices from the MMDental dataset. ChatGPT generated findings text and a provisional normal/abnormal label for every slice (majority vote, three independent readings from the image alone); primary classification performance was assessed on this full, unfiltered set (n=300). A radiologist then independently reviewed each case's image together with ChatGPT's description, producing their own diagnosis; three cases were excluded as insufficient, yielding 297 confirmed cases. On this subset, every model was retrained and re-evaluated under identical 10-fold cross-validation on both the provisional ChatGPT-only labels ("pre") and the radiologist-confirmed labels ("post"), isolating the effect of label provenance from image or architecture. Eight vision architectures, seven language classifiers, and five VLMs were evaluated throughout; three generative models performed exploratory note-drafting. Findings: Raw ChatGPT-generated text produced the highest performance of any modality or condition: language models reached near-ceiling AUC (0.992 to 1.000, n=300), exceeding every vision model (AUC 0.799 to 0.880) and every VLM image-only probe (AUC 0.63 to 0.69). On the 297-case pre/post analysis, this advantage depended heavily on label source: language and text-derived VLM performance fell substantially from ChatGPT-only to radiologist-confirmed labels (e.g. BERT-base AUC 0.999 to 0.837), while vision-model performance was stable or modestly improved (e.g. DenseNet-121 0.867 to 0.891). The radiologist reclassified 62 of 297 cases (21%) relative to ChatGPT's provisional read, and a meaningful proportion of raw ChatGPT text was clinically uninterpretable or unsupported by the imaging. Interpretation: As shown here for the first time, raw, image-derived AI-generated text yields the highest apparent classification performance in this benchmark, but this reflects the text's alignment with its own self-generated labels rather than verified diagnostic content, and a substantial share of that text is not clinically explainable. Radiologist-confirmed text and labels give a lower but trustworthy estimate of true performance, on which convolutional neural network (CNN) vision models remain a stable, comparatively inexpensive baseline. Multimodal dental AI should report performance separately by modality and label provenance rather than pooling headline metrics.

Multimodal large language models (MLLMs) are increasingly applied to image-based clinical reasoning, yet their diagnostic reliability in periodontal image interpretation, and the underlying source of their errors, remain poorly characterized. This study evaluated six architecturally distinct MLLMs (Claude Sonnet 4.5, GPT-5.0, Gemini 2.5, GLM-4.6, Sonar, and Grok 4.1) using 50 image-based multiple-choice questions drawn from the American Academy of Periodontology In-Service Examination, spanning clinical photographs, histopathology, radiographs, cardiac rhythm strips, and anatomical illustrations. A sequential two-phase experimental design was used: in Phase 1, each model independently described each image, selected an answer, and provided a supporting citation; in Phase 2, applied only to questions answered incorrectly, models were given an expert-validated visual description and asked to re-answer, allowing diagnostic improvement through visual correction to be measured directly. Expert ground truth for image content was established by a board-certified periodontist and independently validated by a second board-certified periodontist. Model outputs were classified using a dual-process error taxonomy adapted from Norman's model of diagnostic reasoning, distinguishing perceptual errors, arising from inaccurate visual feature extraction, from cognitive errors, arising from flawed reasoning despite accurate perception, with cognitive errors further subdivided into correctable and persistent subtypes, and additional categories capturing compound perceptual-cognitive failures and compensatory reasoning that overcame inaccurate perception. Diagnostic accuracy and error type distribution varied significantly across models and image modality. Correcting inaccurate visual descriptions in Phase 2 improved diagnostic accuracy for a subset of previously incorrect responses, indicating that a meaningful share of errors originated at the level of visual perception rather than clinical reasoning; conversely, a distinct subset of errors persisted despite accurate corrected visual input, indicating reasoning-level failures independent of perceptual accuracy. Some models also reached correct answers despite generating inaccurate image descriptions, reflecting compensatory reasoning resilient to perceptual error. These findings show that aggregate accuracy scores conflate mechanistically distinct failure modes, and that perceptual and cognitive errors carry different implications for how MLLMs might be safely deployed or improved for diagnostic image interpretation. The expert-guided visual correction framework introduced here provides a generalizable, mechanism-based approach to benchmarking multimodal AI diagnostic performance that extends beyond periodontics to other visually driven diagnostic domains in medicine. As MLLMs become increasingly accessible to clinicians, residents, and dental educators, distinguishing perceptual from cognitive failure is essential for guiding responsible clinical use, targeting model refinement, and informing AI-augmented dental education and competency assessment.

This study compared and evaluated two widely used deep learning-based artificial intelligence (AI) models, U-Net++ and YOLOv11, for quantifying tooth and root exposure on intraoral camera images of the mandibular anterior lingual region. Intraoral images of the mandibular anterior lingual region were collected from 291 patients (mean age, 52.8 years) at a university hospital dental clinic with institutional review board approval (YUMC IRB 2021-07-019-002). A total of 266 eligible images (mean, 5.50 teeth per image; 3.70 teeth with root exposure) were annotated. YOLOv11 and U-Net++ were fine-tuned using five-fold cross-validation with data augmentation. Model performance was evaluated on a held-out test set of 40 images using Dice coefficient, Intersection over Union (IoU), accuracy, mean Average Precision at an IoU threshold of 0.5 (mAP50), Lins concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC). Confidence intervals were estimated using 10,000 bootstrap iterations. For tooth segmentation, U-Net++ demonstrated superior performance, with high accuracy (0.981), Dice coefficient (0.971), and IoU (0.944). In contrast, for root segmentation, YOLOv11 outperformed U-Net++, achieving higher Dice (0.860 vs. 0.746) and IoU (0.762 vs. 0.631). Notably, YOLOv11 showed stronger agreement with the ground truth for quantifying the exposed root ratio (ERR) (CCC, 0.973; ICC, 0.975). These findings suggest that accurate detection of root exposure is important for assessing periodontal tissue loss and that YOLOv11 is a promising model for root exposure quantification in intraoral images. YOLOv11-based quantification of root exposure may serve as a useful adjunctive AI tool for screening and monitoring periodontal conditions and may support individualized treatment planning.

Objectives: Collaboration among health care professionals and the services they provide can be strengthen by interprofessional education (IPE). IPE can be implemented at the undergraduate level. Accordingly, the objective of the present study was to evaluate senior students' understanding of the terms interdisciplinarity and multidisciplinarity within the context of IPE. Methods: A retrospective cross-sectional study design was used. Students understanding of interdisciplinarity and multidisciplinarity was assessed through an assessment question completed by three consecutive cohorts of senior undergraduate dental students at the UBC Faculty of Dentistry between 2021/22 and 2023/24 (N = 177). Responses had a maximum of 100 words and were categorized into one of four predetermined themes: concordant knowledge (when both definitions were correct), discordant rhetoric (when both definitions were incorrect), switched ideas (when the definitions were reversed), and altered discourse (when the concepts of discipline and specialty were conflated). Descriptive and inferential statistical analyses were performed using SPSS Version 31. Results: Of the 177 students enrolled, 164 provided responses to the question on multidisciplinarity and interdisciplinarity: 60 students in 2021/22, 51 students in 2022/23, and 53 students in 2023/24; the mean age was 25 years and 88 were female. Of the four predetermined themes, 45.7% of responses reflected concordant knowledge, 15.9% discordant rhetoric, 18.3% switched ideas, and 20.1% altered discourse. The logistic regression analysis showed age associated with a higher probability of providing the correct definitions (adjusted OR = 1.39; 95% CI: 1.06 - 1.83; p = 0.017). Conclusions: Knowledge of interdisciplinary and multidisciplinary appeared to be retained by the majority of students. However, dental education programs, alongside other health professional training programs, should continue to incorporate interprofessional education through both didactic and experiential learning opportunities to equip students with the skills necessary to provide collaborative care in future practice.

A dental AI system and a dentist reading the same radiographs form a paired comparison. Published comparative studies often report the two arms separately against a reference standard, leaving the joint pattern of correctness between them unavailable for secondary paired inference. We show what that omission costs. The accuracy difference remains exactly identified; its sampling variance does not, so the report contains the estimate and not its uncertainty. On a study of 282 units, two published accuracies are consistent with 38 distinct joint tables whose confidence intervals differ in width by a factor of 2.5. The consequence is a three-zone decision map rather than a single threshold: differences at or below 1.06 points are non-significant under every compatible table, differences at or above 6.03 points are significant under every compatible table, and in between the published numbers cannot decide. We then show the omission is repairable at negligible cost. One additional integer, the number of units both arms classify correctly, identifies the joint table exactly and restores standard paired inference. For a panel of readers the pairwise dependences must arise from one joint distribution, a constraint that binds once three readers are present; publishing each reader's joint-correct count against a single reference reader cannot widen and may tighten every pairwise bound, and in a 7-arm experiment reduced them by a median of 37% even for pairs excluding that reference. Where the integer was never published we give DentalPair-Cert, an interval with finite-sample coverage uniformly over every admissible within-unit AI-dentist dependence under the independent-sampling-unit model, certified in both the nuisance maximization and the inversion. Across 4,200,000 simulated comparisons an independence analysis falls to 74.5% coverage with 12.2% type-I error; in a purposive sample of 9 recent comparative studies, 1 reported a paired test on discordant units.

Background: Children in Central Africa face compounded vulnerabilities, yet no comprehensive review has synthesized evidence on ECC prevalence or risk factors. This study aims to determine the prevalence and identify risk factors associated with early childhood caries (ECC) in the Central Africa Sub-region. Methods: This systematic review and meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the ID CRD420251019424. A comprehensive literature search was conducted across PubMed, African Journals Online, Scopus, Web of Science, CINAHL, and ProQuest as well as through Google Scholar and other grey literature sources, with no language restrictions. Eligible studies were limited to cross-sectional, cohort, case-control, and clinical trials that reported baseline data on the prevalence and risk factors of ECC in the Central Africa sub-region among children under the age of six. There was no restriction on publication date. Exclusion criteria included letters to the editor, commentaries, studies without accessible full texts, systematic reviews, reviews lacking original data, studies with overlapping data already included, and non-peer-reviewed sources such as books. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tool. A random-effects meta-analysis was conducted using Review Manager 5.4.1, and heterogeneity was assessed using the I statistic. Results: Five studies met the eligibility criteria, encompassing 640 participants. Three of the studies were from Cameroon, and one from the Democratic Republic of Congo and the Central African Republic, respectively. One study that did not report the prevalence of ECC and another non-peer-reviewed publication were excluded from the meta-analysis. The prevalence of ECC in the region ranged between 29.4% and 80% with a pooled prevalence of 57% (95% CI: 22 to 92%; I = 98%). Identified risk factors for ECC were related to oral hygiene behaviours, diet and feeding practices, parental/caregiver factors, and access to dental care. Conclusions: ECC is highly prevalent in Central Africa, driven by preventable factors. Urgent region-specific policies and programs are needed to improve preventive services and address data gaps across this under-resourced subregion.

Objectives: Oral health conditions impact a significant proportion of the global population. Chronological age is a known risk factor; however, characterization of epigenetic age remains limited and is expected to provide additional insight into biological mechanisms. Materials and Methods: The National Health and Nutrition Examination Survey (NHANES) was used to analyze the effect of epigenetic age measures of DunedinPoAm, and epigenetic age acceleration (EAA) of Horvath, Hannum, Weidner, Lin, VidalBralo, PhenoAge, GrimAge, and GrimAge2, on various oral health outcomes from survey and examination results. Univariable and multivariable logistic regression were performed, adjusting for sex, race-ethnicity, education, poverty income ratio categories, and dental insurance coverage status. Results: DunedinPoAm was associated with the last dental appointment being for an existing issue (p=0.0093), poor general oral condition (p=0.0226), limiting food due to teeth problems (p=0.0031), and recommendation to see a dentist within the next two weeks (p=0.0171). EAAs for PhenoAge, GrimAge, and GrimAge2, were associated with a smaller number of oral health outcomes, whereas EAAs for Horvath, Hannum, Weidner, Lin, and Vidal-Bralo showed no associations. Conclusions: In a representative U.S. population, DunedinPoAm was most consistently positively associated with different adverse oral health outcomes compared with other epigenetic aging measures. Tracking specific epigenetic ages such as DunedinPoAm, EAA GrimAge, EAA GrimAge2, and PhenoAge, may aid in additional monitoring of oral health outcomes. Understanding specific aging-related CpGs associated with oral health may aid in elucidating underlying molecular mechanisms.

Background/Objectives Dental anxiety remains a significant psychological barrier to oral healthcare, particularly for invasive surgical procedures such as tooth extraction. Aromatherapy using Lavandula angustifolia (lavender) has been proposed as a non-pharmacological adjunct to manage acute preoperative anxiety. This clinical trial evaluated the efficacy of inhaled lavender essential oil on anxiety severity in patients awaiting elective single-tooth extraction. Methods An assessor-blinded randomized controlled clinical trial with limited participant blinding (inherent to the recognizable scent of the intervention), registered with the Iranian Registry of Clinical Trials (IRCT20190920044824N1), was conducted among 60 patients requiring non-surgical tooth extraction. Participants were randomly assigned to an intervention group (n=30), receiving two drops of pure lavender essential oil on a sterile gauze for 20 minutes of inhalation, or a control group (n=30), receiving sweet almond oil as an olfactory placebo. The primary outcome was the severity of somatic anxiety symptoms, measured pre- and post-intervention using the Beck Anxiety Inventory (BAI). Because a significant baseline-by-treatment interaction was detected, a General Linear Model (GLM) retaining this interaction term was used to estimate adjusted group means at the grand-mean baseline, with bootstrap 95% confidence intervals (10,000 resamples). Two pre-specified checks of robustness were performed: (i) an unadjusted, baseline-naive Mann-Whitney U comparison of raw post-intervention scores, and (ii) a sensitivity analysis re-fitting the adjusted model after excluding two baseline outliers identified by inspection of model residuals. Results There were no significant baseline differences between the intervention and control groups regarding sex distribution (p=0.100), mean age (p=0.479), or pre-intervention BAI scores (Mann-Whitney p=0.215). A significant baseline-by-treatment interaction was detected (p<0.001). In the full sample (n=60), the baseline-adjusted GLM estimated a lower post-intervention BAI mean in the intervention group (22.38) than the control group (23.09), an adjusted difference of -0.71 (bootstrap 95% CI: -1.41 to -0.06; p=0.028). However, this signal was not robust: after excluding two patients identified as outliers in the model residuals (n=58), the adjusted difference attenuated to -0.51 and was no longer statistically significant (bootstrap 95% CI: -1.11 to 0.10; p=0.098). The unadjusted comparison of raw post-intervention scores was likewise not significant in the full sample (Mann-Whitney p=0.754). Conclusions A baseline-adjusted analysis of this trial produced an initial signal suggesting a small anxiolytic effect of inhaled lavender, but this signal was not stable under a pre-specified outlier-exclusion sensitivity analysis and was not supported by the unadjusted comparison of raw scores. Taken together, the totality of evidence from this trial is insufficient to conclude that inhaled lavender aromatherapy produces a reliable reduction in acute preoperative dental anxiety. Beyond its clinical findings, this trial offers a concrete, quantified illustration of how baseline imbalance and a small number of influential observations can generate a fragile, model-dependent treatment signal in a modestly sized randomized trial, a methodological caution directly relevant to the design and analysis of future small RCTs in dental and complementary medicine research.