Caries Research最新文献

Introduction: Clinical plaque indices are well-established methods for quantifying the effects of plaque control, for example, with different toothbrush types. However, effect sizes observed in such studies depend on the index used. Therefore, this study aimed to evaluate how Turesky-modified Quigley-Hein Plaque Index (T-QHPI), Rustogi-modified Navy Plaque Index (RMNPI), and RMNPI modified by Bretz (RMNPI-Bretz) scores correspond to actual plaque amounts.

Methods: Thirty participants, 24.0 ± 4.1 years old, were included. Following 72 h without oral hygiene and after subsequent habitual toothbrushing, disclosed plaque (Mira-2-Ton®) was captured using intraoral scans (Carestream 3800). Scoring grids corresponding to the indices were projected onto standardised images on the oral and vestibular sides of the Ramfjord teeth (16, 21, 24, 36, 41, and 44) and scored. Afterwards, actual plaque coverage was quantified planimetrically (P%).

Results: All methods yield significant plaque reduction after brushing (P%: 42.1 ± 14.0%, T-QHPI: 32.7 ± 9.5%, RMNPI: 19.7 ± 9.5%, RMNPI-Bretz: 30.2 ± 9.9; p ≤ 0.001 each). However, Bland-Altman analysis revealed distinct systematic and proportional biases in relation to P%. As P% was not linearly related to the index values, equations were derived, enabling the conversion into actual plaque coverage. Those were applied to T-QHPI and RMNPI data from the literature to allow a deeper understanding of effect sizes.

Conclusions: Plaque indices only partially reflect actual plaque coverage. Compared to RMNPI, T-QHPI and RMNPI-Bretz gave closer agreement with the planimetric measurements. These findings highlight the limitations of traditional plaque indices in accurately representing plaque reduction, while offering a new approach to enhance the interpretability of oral hygiene studies.

Introduction: Early childhood caries (ECC) is a significant public health issue, affecting nearly half of children worldwide. Severe ECC (S-ECC) is characterized by smooth-surface caries in children under 3 years old. Early dental eruption has been linked to a higher risk of ECC, as teeth are exposed to cariogenic environments longer. This study analyzes the association between the number of teeth at 12 and 24 months and the presence of S-ECC at 4 years of age.

Methods: Data were obtained from the 2015 Pelotas Birth Cohort, which included all children born in Pelotas, Brazil, in 2015. The number of teeth at 12 and 24 months, reported by mothers, was the primary exposure, and S-ECC at 4 years was the outcome. Sugar consumption was analyzed as a potential mediator. Confounders included maternal education, family income, and child sex. Mediation analysis was conducted using PARAMED, and Poisson regression with robust variance estimated prevalence ratios, adjusting for confounders. A significance level of 0.05 was used.

Results: The number of teeth at 12 and 24 months was positively associated with S-ECC at 4 years. However, the sugar consumption trajectory did not mediate these relationships.

Conclusion: This study highlights a significant association between the number of teeth in early life and S-ECC development, independent of sugar consumption.

Introduction: This study evaluates the reliability of an artificial intelligence (AI) software for detecting approximal caries lesions of different depth on bitewing radiographs.

Methods: A total of 40 bitewing radiographs consisting of 288 teeth (576 approximal surfaces) were selected for analysis. Utilising the International Caries Detection and Assessment System radiographic scoring system, five dentists established a consensus on the assessment of all radiographs, serving as the gold standard of this study. These radiographs were later analysed using an AI software (Nostic software®), and the detection results were compared to the established ground truth.

Results: The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive values, negative predictive values, and F1 scores were computed. A total of 246 surfaces were included for the detection of enamel lesions (D _1-2) while 341 surfaces were assed for dentinal lesions (D _3-4) and for both enamel and dentinal lesions (D _1-4). The accuracy (95% confidence interval) for detecting enamel lesions (D _1-2) was 0.78 (0.72-0.83), for dentinal lesions (D _3-4) was 0.85 (0.80-0.88) and for both enamel and dentinal lesions (D _1-4) was 0.77 (0.73-0.81). Correspondingly, the AUC (95% confidence interval) values for detecting enamel lesions (D _1-2), dentinal lesion (D _3-4) and both enamel and dentinal lesions (D _1-4) were 0.70 (0.65-0.76), 0.81 (0.75-0.87), 0.75 (0.71-0.80), respectively.

Conclusion: In conclusion, the performance of the AI software in detecting proximal caries lesions of varying depths on bitewing radiographs was found to be decent when compared to the gold standard. This AI software has the potential to serve as an effective tool to support diagnosing initial caries in bitewing images for dental practitioners.

Introduction: Deep learning techniques have emerged as promising tools for enhancing the radiographic diagnosis of caries, particularly when utilizing bitewing radiographs.

Methods: Following the PRISMA guidelines, a systematic review was conducted to assess the use of deep learning for caries diagnosis in bitewing radiographs. Literature searches were performed across Web of Science and PubMed databases for studies published before March 2025 that utilized deep learning for caries detection, segmentation, and classification using bitewing radiographs. Data extraction focused on model architectures, dataset characteristics, annotation processes, diagnostic performance metrics, and potential biases, as assessed by the QUADAS-2.

Results: Twenty-three studies met the inclusion criteria, encompassing caries detection, segmentation, and severity classification. The most frequently applied deep learning models were classification models, such as ResNet and detection models, such as YOLO architectures. Dataset sizes varied widely, ranging from 112 to 8,539 images. Most studies reported high diagnostic performance, with accuracies ranging from 70% to 99%. Some AI models outperformed or matched the performance of human experts, particularly in detecting advanced carious lesions. However, considerable variability was observed in model architectures, dataset characteristics, the applied diagnostic performance metrics, and reporting standards. The risk of bias assessment revealed concerns in patient selection, index test interpretation, and reference standards, with all studies rated as having a high risk of bias in at least one domain.

Conclusion: The review identified challenges in currently developed deep learning models regarding methodological heterogeneity, lack of standardization, limited dataset diversity, insufficient clinical validation, and concerns about bias and data transparency. Nevertheless, all studies concluded that deep learning models are promising as an assistive diagnostic tool in caries diagnostics using bitewing radiography.

Introduction: Dental caries is a disease with high prevalence and concern worldwide, especially among the elderly population. Considering the impact of saliva on the disease development process, protein biomarkers may be potentially useful to monitor the clinical course of the disease.

Methods: This study recorded clinical data and conducted a proteomic analysis of the saliva of eighty elderly individuals (60-84 years). Participants were classified into a no active caries (NC) group and low caries activity (LC) and high caries activity (HC) groups. We hypothesized that the protein profile between the groups would be different and could be used to learn about disease progression and identify potential markers for caries severity in the aged population. All participants had their unstimulated total saliva collected for 5 min. Further correlations were tested to find target proteins strongly linked to clinical variables of caries disease, i.e., the visible dental plaque index, root caries experience, salivary flow, buffering capacity and pH, and percentage of sugar intake. To achieve this, groups with different dental caries statuses were compared.

Results: In total, 1,299 proteins were identified, of which 665 could be quantified in all samples. Of these, 16 proteins had statistically different abundances between the groups with and without dental caries activity (p < 0.05; fold-change [FC] 1.23). Furthermore, 31 proteins differentially regulated in saliva, including 22 downregulated and 9 upregulated proteins, were found in the HC group compared with the LC group (p < 0.05; FC 1.23). When correlated with caries disease-related clinical parameters, 87 candidates were identified and 19 maintained significances from the primary data analysis (p < 0.05). In the gene ontology analysis, most of the regulated proteins were related to innate immunity processes, calcium ions activity, action against bacteria, and cell adhesion.

Conclusion: This study identified potential proteins that may help distinguish different caries activity status on people over 60 years of age. Ultimately, future studies could investigate whether the differential abundance of regulated proteins is a result of the caries process itself or the underlying factors that contribute to its development.

Introduction: This study conducted a molecular dynamics simulation to elucidate the interactions in silico between a sugarcane cystatin (CaneCPI-5) and hydroxyapatite, aiming to deepen the understanding of its potential role in modulating the dental enamel demineralization process.

Methods: The CaneCPI-5 protein structure was modeled using homology with SWISS-MODEL, based on the multicystatin template PDB-ID 4LZI. Molecular dynamics simulations were conducted with the protein positioned randomly in a box containing a hydroxyapatite surface (001 face). Molecular dynamics simulations were then carried out for 500 ns with control of temperature and pressure using the GROMACS package. The free energy of interaction between CaneCPI-5 and the hydroxyapatite surface was calculated using the MM/GBSA method based on trajectory frames.

Results: For several initial orientations, the protein rapidly migrated to anchor to the surface, ensuring minimal distance between its amino acid residues and the surface. Most residues interacting with the surface phosphates were positively charged. Arginine 81, alanine 105 and arginine 107 were found to be key residues. During the simulations, similar values were observed for all orientations in terms of root-mean-square deviation, radius of gyration, and root mean square fluctuation. The free energy calculation showed strong protein-hydroxyapatite interactions, except for one of the initial orientations.

Conclusion: This study demonstrates that CaneCPI-5 has affinity for hydroxyapatite surfaces, with positively charged residues, particularly arginine. The stable binding observed throughout the simulations suggests that this protein may contribute to mechanisms that influence the integrity of dental enamel in demineralizing conditions.

Introduction: Adverse childhood experiences (ACEs) are negative events occurring before age 18 that may impact health. Children exposed to ACEs often receive inadequate preventive care and have a higher prevalence of diseases like dental caries. However, research on this topic remains limited. This study evaluated the association between ACEs and dental caries in adolescents.

Methods: This is a cross-sectional study nested in a cohort performed with a representative sample of preschools from Santa Maria, southern Brazil. This study used data from the last follow-up, with individuals aged between 14 and 18. Data collection was performed through structured questionnaires administered face-to-face and oral clinical examinations. All research team was previously trained and calibrated. Dental caries was measured by International Caries Detection and Assessment System (ICDAS). ACE was evaluated by questions of the Brazilian version of the Adverse Childhood Experiences-International Questionnaire (ACE-IQ), considering alcohol and drug use, suicidal intent, imprisonment, divorce, or death of family members. The co-occurrence of ACEs was also evaluated. Sociodemographic and behavioral variables were used as covariables. Poisson regression models were used to investigate the associations with results present in rate ratio (RR) and 95% confidence interval (CI).

Results: A total of 406 adolescents were examined. The mean of teeth with dental caries was 1.45 (standard deviation 3.6). The rate of dental caries was 56% higher in adolescents who lived with someone who has been sent to prison (RR: 1.56; 95% CI: 1.02-1.90) and 22% higher in adolescents who witnessed their parents' divorce (RR: 1.22; 95% CI: 1.02-1.45) compared to those counterparts. The other ACEs separately were not associated with dental caries. Adolescents exposed to one, two, or more ACEs concomitantly had significantly higher caries rates compared to those with no exposure.

Conclusion: Individuals who witnessed parental divorce or imprisonment exhibited higher rates of dental caries. Moreover, exposure to one, two, or more concurrent ACEs was associated with higher dental caries rates compared to no exposure. These findings underscore the importance of considering specific ACEs within the family environment when assessing their impact on dental caries.

Introduction: Leveraging data from a cohort study of Quebec youth with a family history of obesity, this study aimed to (i) identify neighbourhood socio-environmental typologies in childhood and (ii) estimate their associations with the incidence of dental caries in adolescence.

Methods: We used baseline (2005-2008) and second follow-up (2012-2015) data from the ongoing QUALITY cohort study in Montreal, Canada, which included 512 children aged 8-10 years with ≥1 biological parent with obesity at baseline. Based on prior literature and data availability, we selected three key neighbourhood-level indicators - social disorder, social deprivation, and material deprivation - measured in both school and residential settings. Latent class analysis was used to derive the socio-environmental typologies by combining social disorder, social deprivation, and material deprivation of the social environment in school and residential neighbourhoods. The outcome was dental caries incidence, assessed as the change in the Decayed, Missing, Filled Surface index. Negative binomial regression was performed to estimate incidence ratios (IRs) and corresponding 95% confidence intervals (CIs).

Results: Only three neighbourhood socio-environmental typologies were identified. Type 1 neighbourhoods: high social disorder, high social deprivation, and low material deprivation; type 2 neighbourhoods: median social disorder, median social deprivation, and median material deprivation; type 3 neighbourhoods: low social disorder, low social deprivation, and high material deprivation. Compared to type 1, the IRs (95% CIs) were 0.9 (0.6-1.2) for type 2 and 0.8 (0.6-1.1) for type 3.

Conclusion: Neighbourhoods with lower social disorder and social deprivation may offer a protective effect against dental caries in youth.

Introduction: Dental caries in childhood remains a major global public health issue. In response to persistently high caries levels among children, Chile implemented a national programme (Sembrando Sonrisas) including daily supervised toothbrushing and biannual fluoride varnish applications in nurseries. This study aimed to examine the association between these interventions and caries experience in preschool children, and to assess related socioeconomic inequalities.

Methods: We analysed aggregated municipality-level data (n = 346) on dental caries outcomes in 5-year-olds, coverage of fluoride varnish applications, delivery of toothbrushing materials (toothbrushes and 1,000 ppm fluoride toothpaste), exposure to community water fluoridation (CWF), rurality, and socioeconomic deprivation. The primary outcome was the caries experience of children covered by the programme since its national rollout in 2015. Univariate and multivariate weighted linear regression models assessed associations between programme interventions and caries experience, adjusting for deprivation and CWF. Socioeconomic inequalities in caries experience were evaluated using the Slope Index of Inequality (SII) and Relative Index of Inequality (RII). The distribution of programme delivery across socioeconomic deciles was assessed using weighted regression models.

Results: Data on 309,360 5-year-olds were included. Complete delivery of toothbrushing materials was associated with lower caries experience (50.1% vs. 55.0%), a 4.9% difference (95% CI: 2.5%, 7.2%) after adjustment. CWF exposure was associated with a 7.5% lower caries experience (95% CI: 4.2%, 10.9%). The combination of both interventions showed the lowest caries levels, with a 13.4% difference compared to areas with neither intervention (95% CI: 7.5%, 19.3%). Fluoride varnish application was not significantly associated with caries experience. Delivery of interventions was equitable across socioeconomic groups. However, caries experience was significantly higher in the most deprived municipalities (SII = 14.7%, 95% CI: 11.7%, 17.7%; RII = 0.283, p < 0.001).

Conclusion: Lower caries experience was associated with supervised daily toothbrushing and CWF exposure, but not with fluoride varnish application. Despite equitable programme coverage, substantial socioeconomic inequalities persist. As this ecological study cannot establish causality, further research is needed to evaluate long-term trends and the role of broader determinants in improving child oral health.

Introduction: This study compared the diagnostic accuracy of interproximal caries detection using intraoral bitewing radiographs, assessed by both human operators and an artificial intelligence (AI) program, a near-infrared reflectance imaging (NIRI) system with operator-conducted assessment, and histological evaluation as the reference.

Methods: 100 posterior teeth with or without caries were mounted on 10 typodonts. Initially, 180 surfaces were examined, but eight were excluded for different reasons. Therefore, 171 proximal surfaces were analyzed. NIRI imaging was performed using the iTero Element 5D®, and radiographs were analyzed by operators and an AI program, Denti.AI. Results were compared with histology, assessing sensitivity (Se), specificity (Sp), positive (PPV) and negative (NPV) predictive values, F1-score, areas under receiver operating characteristic curves (AUCs), and the Fleiss Kappa coefficient (k).

Results: The statistical analysis results for each diagnostic test were as follows: examiner radiographic assessment (Se = 52%, Sp = 84.6%, PPV = 71.6%, NPV = 70.3%, F1-score = 60%, AUC = 0.684, k = 0.459); NIRI (Se = 37%, Sp = 98.9%, PPV = 96.4%, NPV = 67.8%, F1-score = 52%, AUC = 0.673, k = 0.475); AI-guided radiographic assessment (Se = 13.7%, Sp = 95.9%, PPV = 71%, NPV = 59.8%, F1-score = 23%, AUC = 0.548). McNemar's test showed a p < 0.05 for all diagnostic tests.

Conclusion: Both the operator-conducted NIRI system and examiner radiographic assessment demonstrated superior detection capabilities compared to the AI program. Among these methods, the examiner radiographic assessment yielded the best results, followed by the NIRI system, demonstrating its potential for clinical use.