Search Results (137)

Background/Objectives: Accurate assessment of gingival thickness (GT) is important for periodontal and implant treatment planning. Methods: In this method-comparison study based on prospectively collected clinical data from 2012, GT was assessed at standardized buccal sites using cone beam computed tomography (CBCT), bone sounding (BS), an earlier-generation pulse–echo ultrasound (US) device, and direct digital caliper (CA) measurement. Of 53 acquired CBCT scans, 12 (23%) were non-evaluable because the buccal soft tissues could not be reliably delineated, leaving 41 sites from 41 patients for analysis. A ±0.30 mm clinical comparison margin, based on the difference used in the original sample-size calculation, was applied to interpret agreement. Results: Mean comparator-minus-CBCT biases were −0.19 mm for BS (95% limits of agreement [LoA], −0.98 to 0.61 mm), −0.16 mm for US (95% LoA, −0.83 to 0.51 mm), and −0.71 mm for CA (95% LoA, −1.99 to 0.56 mm). Although BS and the tested US device showed smaller average differences from CBCT than CA, the LoA for all comparisons exceeded this margin. Phenotype classification was exploratory because only four sites were classified as thin by CBCT. Conclusions: BS and the tested pulse–echo US device showed smaller average differences from CBCT than CA; however, the limits of agreement for all comparisons exceeded this margin, so none of the comparator methods can be considered interchangeable with CBCT. The 23% non-evaluable CBCT rate and the device- and protocol-specific nature of the findings limit their generalizability to current-generation systems. Full article

Background and Objectives: Caries experience, plaque accumulation, gingival inflammation, and periodontal screening findings may coexist as manifestations of a shared oral-health burden. We examined whether these routinely recorded indicators form distinct multivariable profiles or primarily represent ordered levels along a common burden continuum in children and adolescents. Materials and Methods: This secondary exploratory analysis included 638 participants from a multicenter cross-sectional clinical database: 407 aged 6–12 years and 231 aged 13–19 years. Permanent-dentition Decayed, Missing, and Filled Teeth score, Plaque Index, Gingival Index, and Community Periodontal Index were standardized and analyzed separately by age stratum. Unsupervised K-means clustering compared solutions containing two to five groups to summarize participant-level patterns without imposing predefined clinical categories. Hierarchical Ward classification assessed algorithmic concordance; principal component analysis and a composite standardized burden score assessed dimensionality. Sensitivity analyses excluded the periodontal index or treated it as ordinal with Gower-distance partitioning around medoids, and 1000 repeated 80% subsamples assessed sampling stability using the adjusted Rand index. Results: A single principal component explained 91.0% of variance at 6–12 years and 91.8% at 13–19 years, with all four indicators loading strongly on the same dimension. Ordered cluster membership correlated closely with the composite burden score (Spearman ρ = 0.942 and 0.939; both p < 0.001). The retained low-, intermediate-, and high-burden groupings were highly consistent when the periodontal index was excluded or treated as ordinal and under repeated subsampling. In adolescents, a two-group alternative preserved the low- and high-burden extremes while dividing the intermediate group, indicating that the number of groups changes descriptive granularity rather than revealing a separate phenotype. Conclusions: The principal new finding is that four commonly used oral-health indicators converge on one dominant participant-level burden dimension rather than defining distinct clinical phenotypes. This supports an integrated epidemiological description of cumulative oral-health burden while cautioning against interpreting data-driven groups as diagnostic or treatment categories. The numerical group distributions are sample-specific and require external and longitudinal validation before being transferred to other populations or clinical decision-making. Full article

Background: Periodontitis is a chronic inflammatory disease characterized by progressive destruction of the tooth-supporting tissues and may substantially impair oral health-related quality of life (OHRQoL). Although the clinical consequences of periodontitis are well documented, the extent to which disease severity is associated with patient-reported outcomes remains insufficiently understood. This study aimed to evaluate the association between periodontitis severity and OHRQoL and to examine sociodemographic, behavioral, and systemic factors associated with poorer OHRQoL. Methods: A cross-sectional analytical study was conducted among 136 adult patients diagnosed with stage II or stage IV periodontitis at the Periodontology Clinic of Universidad Tecnológica de México (UNITEC). OHRQoL was assessed using the Oral Health Impact Profile for Periodontal Disease (OHIP-14-PD). Sociodemographic characteristics, smoking status, and diabetes mellitus were recorded using a structured questionnaire. Periodontal status was determined through standardized clinical and radiographic examinations. The internal consistency of the OHIP-14-PD was evaluated using Cronbach’s alpha coefficient. Bivariate analyses were performed using non-parametric tests. An exploratory, partially adjusted multivariable linear regression model including sex, marital status, diabetes mellitus, and periodontitis stage was also performed. Results: Stage IV periodontitis was more prevalent than stage II periodontitis (55% vs. 45%). The mean OHIP-14-PD score was 24.9 ± 13.7, and the instrument demonstrated excellent internal consistency (Cronbach’s α = 0.89). Patients with stage IV periodontitis had significantly higher OHIP-14-PD scores than those with stage II disease (30.1 ± 12.4 vs. 18.6 ± 12.6; p < 0.001), indicating poorer OHRQoL. In the bivariate analyses, poorer OHRQoL was also observed among current smokers, participants with diabetes mellitus, those with lower educational attainment, and those with lower socioeconomic status (p < 0.05). In the exploratory partially adjusted model, male sex (β = 4.2; 95% CI: 0.1–8.2; p = 0.042), diabetes mellitus (β = 6.5; 95% CI: 2.1–10.9; p = 0.004), and stage IV periodontitis (β = 4.6; 95% CI: 0.4–8.7; p = 0.031) were associated with higher OHIP-14-PD scores. Conclusions: In this single-center cross-sectional sample, patients with stage IV periodontitis reported higher OHIP-14-PD scores than those with stage II periodontitis. These findings indicate an association within the studied clinical population and should not be interpreted as evidence of a causal effect or as directly generalizable to broader populations. Larger multicenter longitudinal studies including patients across all periodontitis stages are required to confirm the magnitude and external validity of this association. Full article

Background: Mitogen-activated protein kinase 3 (MAPK3/ERK1) plays a central role in cellular proliferation, inflammation, apoptosis, and survival signalling and has been implicated in oral squamous cell carcinoma (OSCC), periodontitis, oral lichen planus, and other chronic oral inflammatory diseases. The present study employed an integrated computational workflow combining machine learning (ML)-based quantitative structure–activity relationship (QSAR) modelling, molecular docking, density functional theory (DFT), molecular dynamics (MD) simulation, and MM-GBSA analysis to identify and characterise potent MAPK3 inhibitors. Methods: A curated dataset of 907 experimentally validated MAPK3 inhibitors was retrieved from the ChEMBL database and processed using molecular descriptors and Morgan fingerprints. Multiple ML algorithms were evaluated under scaffold-based validation, with Light Gradient Boosting Machine (LightGBM) demonstrating the best predictive performance. Results: The final model achieved strong classification capability with ROC-AUC values of 0.898 and 0.926. Feature importance analysis revealed that local structural motifs captured by fingerprint descriptors played dominant roles in MAPK3 inhibitory activity. The top-ranked compounds were subjected to molecular docking, where compounds 58324148 and 137531515 exhibited strong binding affinities of −11.9 and −11.0 kcal/mol, respectively. DFT calculations demonstrated favourable electronic properties with low HOMO–LUMO energy gaps, while MD simulations confirmed stable receptor–ligand interactions throughout 200 ns trajectories. MM-GBSA analysis further supported strong binding stability dominated by van der Waals interactions. Conclusions: Overall, the integrated computational framework successfully identified promising MAPK3 inhibitor candidates with potential therapeutic relevance for oral inflammatory and proliferative diseases. Full article

Background and Objectives: This study investigated the association between periodontal procedure intensity, serving as an indirect proxy for periodontal disease burden, and incident tinnitus in Korean adults. Materials and Methods: Using the Korean National Health Insurance Service–National Health Screening Cohort (2002–2019), we included adults aged ≥40 years who had a diagnosis of periodontal disease (International Classification of Diseases, 10th Revision [ICD-10] code K05), had undergone relevant periodontal treatment, and had available health-screening data. Because direct clinical periodontal measures were unavailable, participants were classified according to treatment intensity into: a mild procedure group (scaling or root planing only) and a moderate-to-severe procedure group (subgingival curettage, extraction, periodontal flap surgery, bone grafting, or guided tissue regeneration). Incident tinnitus was defined as an ICD-10 H93.1 diagnosis recorded during two or more outpatient visits to a neurology or otolaryngology department to improve diagnostic specificity. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models adjusted for demographic, socioeconomic, cardiometabolic, and behavioral factors. Results: Among 132,808 participants (76,844 mild; 55,964 moderate-to-severe), the incidence rate of tinnitus was higher in the moderate-to-severe group than in the mild group (4.38 vs. 3.87 per 1000 person-years). After adjustment, the moderate-to-severe group showed a significantly higher risk of tinnitus (HR 1.24, 95% CI 1.17–1.30). These associations were consistent across sex- and age-stratified analyses and in sensitivity analyses using stricter tinnitus definitions. Conclusions: Greater periodontal procedure intensity was associated with a modestly higher incidence of tinnitus in this nationwide cohort. Because procedure intensity serves only as an indirect surrogate for periodontal disease burden, and because auditory confounders and short-term procedure-related factors were unavailable, causality and a specific inflammatory mechanism cannot be established. Further studies incorporating direct periodontal measurements, audiological data, and prespecified lag-time analyses are warranted to confirm these findings. Full article

Background. Radiographic bone loss is a primary determinant of periodontitis stage. Intraoral radiographs (periapical and bitewing) are the standard projections for assessing interproximal bone levels, yet their interpretation is subjective and poorly reproducible, and previous syntheses have pooled intraoral with panoramic imaging. Objectives. To appraise and synthesise studies developing or validating deep learning (DL) models for detecting, quantifying, staging or classifying alveolar bone loss on intraoral radiographs. Methods. Seven databases and six supplementary sources were searched from 1 January 2015 to 12 June 2026 (PROSPERO CRD420261455818, registered retrospectively). Two reviewers screened, extracted and appraised in duplicate using QUADAS-2 with AI-specific signalling questions, CLAIM and APPRAISE-AI; certainty was rated by GRADE. Heterogeneity precluded pooling; synthesis was narrative. Results. Sixteen publications reporting 15 independent datasets (2018–2026) were included (11 periapical, two bitewing, three mixed; 39–21,819 radiographs). The architectures employed comprised classification, segmentation, object-detection, keypoint-localisation and transformer-based models. Accuracy for binary detection ranged from 0.73 to 0.97, Dice coefficients reached ≥0.91, and intraclass correlation with expert measurement was 0.75–0.85. Performance fell for multiclass staging, posterior sites and furcations. Only two studies used an external test set; none was prospective; risk of bias was mostly high or unclear. Conclusions. Performance lies within the range observed for calibrated readers, but the evidence is dominated by small, single-centre, retrospective datasets with annotation-based reference standards and almost no external validation; certainty is very low. Deep learning is best regarded as a clinician-supervised adjunct for screening, triage and quality assurance rather than an autonomous diagnostic device. Full article

Background/Objectives: Endo-periodontal lesions (EPLs) represent complex pathological entities involving both periodontal and pulpal tissues, often implying diagnostic and therapeutic challenges. Limited evidence exists regarding the relationship between periodontal disease staging and grading and EPL subtype. The aim is to evaluate the clinical and statistical distribution of endo-periodontal lesions and investigate their association with periodontal stage and grade according to the European Federation of Periodontology (EFP) classification. Methods: An observational retrospective study was conducted in the Department of Periodontology, Faculty of Dental Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania. Among 884 examined patients, 31 of them were diagnosed with EPLs and included in the analysis. Demographic variables, systemic conditions, affected tooth type, EFP stage and grade, and EPL type according to the Foce classification were recorded. Statistical analysis included Fisher’s exact test and univariable and multivariable binomial logistic regression models. Results: The mean age of the patients was 53.55 ± 9.42 years, with a nearly equal gender distribution. Most patients presented Stage III periodontitis (54.8%), Grade B periodontitis (71%), and EPL 3 (58.1%). Pluriradicular teeth were the most frequently affected (64.5%). The only statistically significant association identified was between EFP grading and EPL classification (p = 0.001). Grade B patients were significantly more likely to present EPL 3 than Grade C patients. Logistic regression analysis demonstrated that Grade B was a strong predictor of EPL 3 both in the univariable model (OR = 27.2; 95% CI: 2.712–272.828) and after adjustment for age and gender (OR = 32.676; 95% CI: 2.832–376.984). Neither age nor gender showed a significant association with EPL 3 occurrence. Conclusions: EPL 3 was the predominant lesion subtype in the study. EFP grading was significantly associated with EPL distribution, with Grade B emerging as an independent predictor of EPL 3. These findings suggest that periodontal disease progression may play a key role in the development and characterization of combined endo-periodontal lesions and highlight the importance of integrated periodontal–endodontic assessment in clinical practice. Full article

Background: Periodontitis and arterial hypertension are highly prevalent chronic conditions that share common risk factors and inflammatory pathways. Increasing evidence suggests a potential association between both diseases; however, the strength and consistency of this relationship remain unclear. Methods: A systematic review and meta-analysis of observational studies was conducted following PRISMA guidelines. Electronic searches were performed in PubMed (MEDLINE), Scopus, and the Cochrane Library without date restrictions. Studies evaluating the association between periodontitis and arterial hypertension in humans were included. Risk of bias was assessed using the Newcastle–Ottawa Scale. A random-effects meta-analysis was performed using adjusted odds ratios (ORs). Results: Thirty-six studies were included in the systematic review; of these, 11 studies reporting a directly comparable, adjusted odds ratio were included in the quantitative meta-analysis, comprising a total of 48,761 participants. The pooled analysis demonstrated a statistically significant association between periodontitis and hypertension (OR = 1.65; 95% CI: 1.42–1.93; p < 0.00001). Heterogeneity was substantial (I² = 65%) but was partly explained by periodontal case definition, with a stronger association among studies using the AAP/EFP classification (OR = 1.90; 95% CI: 1.57–2.30) than those using alternative periodontal indices (OR = 1.35; 95% CI: 1.21–1.50). The overall certainty of evidence, assessed using GRADE, was rated as Very Low. Conclusions: This systematic review and meta-analysis identifies a consistent positive association between periodontitis and arterial hypertension across observational studies. Given the very low certainty of the underlying evidence, this finding should be interpreted as a signal warranting further investigation rather than a robust, settled conclusion. Causality cannot be established from the available observational evidence, and the potential relevance of periodontal health to cardiovascular risk merits further research. Full article

Background: Periodontal diseases continue to be highly prevalent worldwide, and their early detection represents a clinical challenge, especially when based on non-standardized intraoral photographs. The present study develops an artificial intelligence-based prototype for the automatic classification of periodontal health, gingivitis, and periodontitis using Red, Green, Blue (RGB) images obtained in real conditions. Methods: A dataset comprising 1552 (306 healthy, 1019 with gingivitis, and 227 with periodontitis) was constructed by integrating proprietary clinical photographs with a public repository. A patient-level stratified split was enforced to prevent data leakage, ensuring that all images from the same patient remained within a single partition. This proposal uses EfficientNet-B2, which includes two-phase training, balanced focal loss, weighted sampling, CutMix/MixUp augmentation, and centered anatomical cropping to improve generalization across varied images. Results: The final model achieved an accuracy of 0.833, a macro F1-score (F1) of 0.832 [95% CI: 0.789–0.874], and a macro Area Under the Curve (AUC) of 0.962 [95% CI: 0.946–0.976] on an independent test set. A seven-configuration ablation study showed that each training component contributes to improved performance, and a baseline comparison with ResNet-50 demonstrated the superiority of EfficientNet-B2. Five-fold cross-validation with patient-level grouping yielded consistent results (F1 = 0.832 ± 0.016, AUC = 0.950 ± 0.005). Conclusions: These results demonstrate that EfficientNet-B2 is useful for assessing periodontal health using readily available RGB photographs, with potential for early detection, clinical triage, and remote assessment in modern dentistry. Full article

Accurate assessment of radiographic bone loss (RBL) is essential for periodontal diagnosis and staging; however, manual measurement from dental radiographs is labor-intensive, time-consuming and subject to inter- and intra-examiner variability. Existing AI-based methods primarily formulate bone loss assessment as classification, landmark prediction, or direct segmentation of thin anatomical structures, limiting measurement interpretability and robustness. This study proposes clinically interpretable two-phase framework for automated and clinically interpretable RBL estimation from periapical radiographs. The framework explicitly separates anatomical structure recognition from geometric measurement, improving transparency and reducing error propagation. In the first phase, deep learning models segment key anatomical structures, including the crown, root, third root and alveolar bone. In the second phase, a deterministic geometric algorithm extracts clinically relevant landmarks, including the cemento–enamel junction (CEJ), bone crest, and root apex, and computes root length, CEJ–bone crest distance, and radiographic bone loss following established periodontal measurement principles. The framework was evaluated on a curated dataset of annotated radiographs. DS-TransUNet achieved the best segmentation performance. Quantitative evaluation yielded mean absolute errors of 0.81 mm for CEJ–bone crest distance, 0.71 mm for root length, and 5.89% for RBL estimation. Bland–Altman analysis demonstrated minimal systematic bias (−1.03%) and good agreement with expert measurements across different disease severities, supporting the framework’s potential as an objective and clinically applicable tool for periodontal bone loss assessment. Full article

Oral diseases, encompassing conditions such as periodontitis, head and neck squamous cell carcinoma, pulpitis, and mucosal infections, remain a major global health burden due to their high prevalence and complex, multifactorial pathophysiology. The unique anatomical structure of the oral cavity, together with persistent microbial challenges and dynamic immune responses, imposes substantial limitations on conventional drug delivery strategies. Biomimetic cell membrane-based materials have recently emerged as a promising class of delivery platforms, leveraging natural biological interfaces to confer inherent biocompatibility, immune evasion, prolonged circulation, specific targeting, and biofilm-interactive capabilities. These features position them as a transformative approach for improving therapeutic precision and efficacy in oral disease management. In this review, we provide a systematic and materials-oriented overview of biomimetic cell membrane-based drug delivery systems. Specifically, we discuss: (1) the biological sources, classification, and physicochemical properties of membrane-coated systems, along with their fabrication and engineering strategies; (2) the mechanistic basis of targeting, immune modulation, and nanobiointerface interactions, and their applications across representative oral diseases; and (3) current challenges, including scalable manufacturing, functional controllability, biosafety, and clinical translation. Furthermore, we highlight emerging directions such as stimuli-responsive membrane systems and multifunctional integrated platforms, aiming to provide a conceptual framework for the rational design and clinical advancement of biomimetic drug delivery systems in complex disease settings. Full article

Smoking and central obesity have both been linked to periodontitis, but their combined relationship with periodontal disease may be influenced by demographic and behavioral factors. This cross-sectional study analyzed records of 420 adult dental patients attending the College of Dentistry at the University of Baghdad. Data included demographic characteristics, smoking status, periodontal clinical findings, body mass index (BMI), and waist-to-height ratio (WHtR). Periodontitis was defined according to the 2018 classification framework, and logistic regression models were used to examine the associations of smoking and obesity-related indicators with periodontitis. The overall prevalence of periodontitis was 36.4%. Participants with periodontitis were significantly older than those without periodontitis (46.0 vs. 28.9 years; p < 0.0001). In the fully adjusted model, age remained the strongest factor associated with periodontitis (OR = 1.15 per year; 95% CI: 1.11–1.18; p < 0.001). The apparent association between smoking and periodontitis was substantially influenced by age, as current smoking was more common among younger participants in this sample. The association between smoking status and periodontitis appeared to differ according to WHtR category (interaction term p = 0.016); however, this finding should be interpreted cautiously because of the cross-sectional design and age imbalance across exposure groups. Overall, the findings suggest that age was the dominant factor associated with periodontitis in this dental patient sample, while the relationship between smoking, central obesity, and periodontitis requires further investigation in longitudinal studies with detailed smoking and metabolic data. Full article

Background/Objectives: Periodontitis is a highly prevalent chronic disease requiring accurate diagnosis for effective treatment planning. Artificial intelligence (AI) has emerged as a potential tool to support clinical decision-making. This study aimed to compare the diagnostic performance and classification error patterns of a large language model (LLM) and a neural network (NN) in periodontitis classification according to the current staging and grading system. Methods: This retrospective study included 110 patients with periodontal disease. Clinical and demographic variables (age, sex, smoking status, number of teeth, API, BOP, PPD, and CAL) were analyzed. Reference diagnoses were established by two experts. Cases were evaluated using an LLM and a neural network. Model performance was assessed using accuracy, confusion matrices, and Cohen’s kappa coefficient, along with error analysis. Results: The LLM achieved 62% accuracy for stage and 63% for grade classification (κ = 0.48). The neural network showed higher performance, with 85% accuracy for stage and 79% for grade (κ = 0.79 and κ = 0.67, respectively). The LLM more often underestimated disease severity, whereas the neural network tended to overestimate progression. Differences between models were statistically significant (p < 0.0001). Conclusions: In this dataset and classification task, the task-specific neural network demonstrated higher diagnostic performance than the evaluated large language model. However, the findings should be interpreted in light of the fundamentally different training paradigms and intended applications of these AI systems. Further research is required to optimize and validate AI-based approaches for clinical use. Full article

The 2018 classification of periodontal and peri-implant diseases introduced a multidimensional diagnostic framework integrating staging, grading, and disease extent, representing a major advance over earlier severity-based systems. By incorporating structural destruction, treatment complexity, spatial distribution, and estimated risk of progression, the classification aimed to support more individualized and biologically informed diagnosis. However, increasing clinical application has revealed interpretive challenges, particularly in cases where different components of the system appear discordant. This perspective examines these challenges through a conceptual and clinical lens, focusing on the distinction between focal severity and overall disease burden in staging, the biological meaning of disease distribution, the interpretation of tooth loss as a historical rather than current indicator of disease status, and the need to differentiate between observed progression and risk-based modifiers in grading. Rather than reflecting deficiencies of the classification itself, these discordances are understood as a consequence of applying categorical systems to a biologically heterogeneous and temporally dynamic disease. A biologically grounded interpretive hierarchy is proposed, prioritizing observed tissue behavior and realized tissue destruction over probabilistic risk indicators while integrating structural parameters, historical outcomes, and susceptibility modifiers within their appropriate conceptual roles. This approach enhances diagnostic coherence and supports a more phenotype-oriented interpretation of periodontal disease. Full article

Background/Objectives: In 2018, a classification system for periodontal and peri-implant diseases and conditions defined an endo-periodontal lesion (EPL) as a pathological communication between the endodontic and periodontal tissues of a given tooth. As the evidence to define the etiology, diagnosis, prognosis and treatment was considered limited, a cross-sectional study was carried out to evaluate its prevalence in a population treated at the Periodontics Department of Faculty of Dentistry of University of Buenos Aires (FOUBA). The primary objective was to evaluate the prevalence of EPL. The secondary objective was to identify potential risk indicators associated with their prevalence. Methods: Patients referred for first time to the Periodontics Department of FOUBA during April to June 2025 were consecutively selected. Clinical and radiographic examination was carried out. Categorical outcomes were described using proportions. The crude association between the prevalence of EPL and each of the recorded factors was determined by means of the chi-square test and a logistic regression analysis. Results: A total of 182 participants (128 women and 54 men) with a mean age of 50.8 (standard deviation = 15.6) years were included. The prevalence of participants with EPL was 14.8%. The average was 1.7 teeth per participant with a minimum of 1 tooth and a maximum of 5 teeth. In total, 85.2% of participants with EPL had stage III–IV generalized periodontitis, grade B or C. The logistic regression analysis identified periodontitis stage III–IV (OR = 5.9; 95% Confidence interval [1.9: 18.9] (p = 0.003)) as a potential risk indicator for EPL. Conclusions: The prevalence of participants with EPL in a teaching hospital of the University of Buenos Aires was 14.8%. EPL was more frequently found in participants with periodontitis stage III–IV. Periodontitis stage III–IV was considered a potential risk indicator of EPL. Full article