Caries Risk Prediction Research Articles

Assessment of overall caries risk and mode of birth delivery in infants and children with and without ECC: a cross-sectional study.

The study assessed the caries risk in infants and children with and without early childhood caries (ECC) using American Academy of Pediatric Dentistry Caries Risk Assessment Tool (AAPD CRAT) and identified the association between caries risk and mode of birth delivery. This cross-sectional study included 96infants and childrenwith all maxillary and mandibular primary anterior teeth and at least two primary molars erupted clinically in the oral cavity. The details on the mode of birth delivery were noted in a preformed template along with the demographic details, followed by caries risk assessment (CRA) to assess the caries risk of an individual. The outcome assessment for overall caries risk was performed for the ECC and non-ECC group and compared against the mode of birth delivery. Descriptive statistics were performed for all the study variables (gender, overall caries risk, mode of birth delivery, and term of delivery). The Chi-square test was used to compare the categorical variables between the two groups (ECC and non-ECC groups). The high caries risk category contributed to 52.1% and 68.8% in the ECC and non-ECC groups, respectively. The prevalence of vaginal mode of delivery was 41.7% and 52.1% in the ECC and non-ECC groups, respectively. Caries risk increases with increasing age and the non-ECC group shows more risk (68.8%) than the ECC group. In comparison with the variables against the groups (ECC and non-ECC), Chi-square test revealed that only the term of delivery (p value = 0.035) and overall caries risk (p = 0.045) showed statistically significant difference. The proportion of high caries risk subjects was significantly more in the non-ECC groups. Children delivered by the C-section mode showed high caries risk (66.7%) when compared with the vaginal mode of delivery (53.3%). A validated CRAT for predicting caries risk is required.

Salivary Proteins and Metabolites as Caries Biomarkers in Adolescents.

The identification of salivary molecules that can be associated to dental caries could provide insights about caries risk and offer valuable information to develop caries prediction models. However, the search for a universal caries biomarker has proven elusive due to the multifactorial nature of this oral disease. We have therefore performed a systematic effort to identify caries-associated metabolites and proteins in saliva samples from adolescents that had a caries experience and those that were caries-free. Quantification of approximately 100 molecules was performed by the use of a wide range of techniques, ranging from nuclear magnetic resonance metabolomics to ELISA, Luminex or colorimetric assays, as well as clinical features like plaque accumulation and gingival index. In addition, simplified dietary and oral hygiene habits questionnaires were also obtained. The caries-free group had significantly lower consumption of sweetened beverages and higher tooth brushing frequency. Surprisingly, very few compounds were found to individually provide discriminatory power between caries-experienced and caries-free individuals. The data analysis revealed several potential reasons that could underly this lack of association value with caries, including differences in metabolite concentrations throughout the day, a lack of correlation between metabolite concentrations in plaque and saliva, or sex-related differences, among others. However, when multiple compounds were combined by multivariate analysis and random forest modeling, a combination of 3-5 compounds were found to provide good prediction models for morning (with an AUC accuracy of 0.87) and especially afternoon samples (AUC = 0.93). While few salivary biomarkers could differentiate between caries-free and caries-experienced adolescents, a combination of markers proved effective, particularly in afternoon samples. To predict caries risk, these biomarkers should be validated in larger cohorts and longitudinal settings, considering factors such as gender differences, and variations in oral hygiene and diet.

Analysis of Machine Learning-Based Dental Caries Risk Prediction Model at Cairo Hospital, Egypt

Analysis of Machine Learning-Based Dental Caries Risk Prediction Model at Cairo Hospital, Egypt

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

BackgroundThe aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors.MethodsThis study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson's correlation test was also conducted to assess the correlation between participants' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset.ResultsA statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%.ConclusionsCaries risk assessment using a simple questionnaire can identify individuals at risk of dental caries, determine the key risk factors, provide information to help reduce the risk of dental caries over time and ensure follow-up. In addition, it is extremely important to apply effective preventive treatments and to prevent the general health problems that are caused by the deterioration of oral health. The results of this study show the potential of machine learning algorithms for predicting caries risk groups, and these algorithms are promising for future studies.

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques.

Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.

Prediction of Future Caries in 1-Year-Old Children via the Salivary Microbiome

Dental caries is the most common chronic disease in children that causes negative effects on their health, development, and well-being. Early preventive interventions are key to reduce early childhood caries prevalence. An efficient strategy is to provide risk-based targeted prevention; however, this requires an accurate caries risk predictor, which is still lacking for infants before caries onset. We aimed to develop a caries prediction model based on the salivary microbiome of caries-free 1-y-old children. Using a nested case-control design within a prospective cohort study, we selected 30 children based on their caries status at 1-y follow-up (at 2 y old): 10 children who remained caries-free, 10 who developed noncavitated caries, and 10 who developed cavitated caries. Saliva samples collected at baseline before caries onset were analyzed through 16S rRNA gene sequencing. The results of β diversity analysis showed a significant difference in salivary microbiome composition between children who remained caries-free and those who developed cavitated caries at 2 y old (analysis of similarities, Benjamini-Hochberg corrected, P = 0.042). The relative abundance of Prevotella nanceiensis, Leptotrichia sp. HMT 215, Prevotella melaninogenica, and Campylobacter concisus in children who remained caries-free was significantly higher than in children who developed cavitated caries (Wilcoxon rank sum test, P = 0.024, 0.040, 0.049, and 0.049, respectively). These taxa were also identified as biomarkers for children who remained caries-free (linear discriminant analysis effect size, linear discriminant analysis score = 3.69, 3.74, 3.53, and 3.46). A machine learning model based on these 4 species distinguished between 1-y-old children who did and did not develop cavitated caries at 2 y old, with an accuracy of 80%, sensitivity of 80%, and specificity of 80% (area under the curve, 0.8; 95% CI, 44.4 to 97.5). Our findings suggest that these salivary microbial biomarkers could assist in predicting future caries in caries-free 1-y-old children and, upon validation, are promising for development into an adjunctive tool for caries risk prediction for prevention and monitoring.

Identifying high-risk children for dental caries in school settings: A simple predictive model.

Untreated caries in permanent teeth is the most prevalent condition worldwide. Use of a simple, validated caries risk prediction tool will offer a low-cost mechanism to identify high-risk children for targeted preventive programmes. To develop and validate a caries risk prediction model for 5-6-year-old Sri Lankan children. Methods Two case-control studies were done for model development and validation. Cases and controls were defined as 8-9-year-olds with and without permanent tooth caries respectively. Based on dental records and confirmation by clinical examination, 120 cases and 360 controls for model development, and 100 cases and 100 controls for model validation were selected. Data was collected using dental records and a pretested parental self-administered questionnaire. Risk predictors were identified by logistic regression analysis. Cut-off point was determined by plotting a ROC curve. Four risk predictors were identified: 'having 5 or more posterior decayed teeth' (OR= 2.1, 95% CI: 1.0 - 4.4), 'brushing frequency of once or less' (OR= 3.5, 95% CI: 2.1 - 6.0), 'not using fluoridated toothpaste' (OR= 3.2, 95% CI: 1.8 - 5.6) and 'consuming more than two snacks containing fermentable carbohydrates in between meals' (OR= 1.6, 95% CI: 0.9 - 2.9). A 10-point score was developed. Following external validation, a sensitivity of 31% (95% CI: 22.1% - 41.0 %) and a specificity of 87% (95% CI: 78.8% - 92.9 %) was obtained for a cut-off value of 2.5. The model could be used to identify high-risk children, especially in areas with higher disease burdens.

Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm

BackgroundAn easily accessible caries risk prediction model (CRPM) based on nonbiological predictors is lacking. Developing a CRPM for community screening is essential for children's dental health promotion by a public health approach. ObjectiveThis study aimed to develop and validate a caries risk prediction model (CRPM) for children using a machine learning algorithm based on dental care behavioral factors and other nonbiological factors using a 3-month multicenter cohort. MethodsChildren aged 12 months to 60 months were recruited at three primary care settings and three kindergartens in Chengdu, China. Dental examination was conducted for all enrolled children by calibrated pediatric dentists at baseline and three months later. All parents of the enrolled children were asked to complete a questionnaire with dental-related information. Machine learning algorithms, including random forest, logistic regression, and adaptive boosting, were used to develop a prediction model. Sensitivity, specificity, accuracy, precision, negative predictive value and F-score were reported to estimate the internal validation of the models. ResultsA total of 481 out of 745 children without a history of caries experience at baseline remained for analysis. In the total sample population, 236 (49.1%) children were female, and the mean age was 31.2 months. During the follow-up exams, 66 (13.6%) children had new-onset caries. The child's age, height, weight, family caries status, brush teeth two minutes per time, fluoride toothpaste usage, brushing twice per day, parental monitoring brushing teeth, mother delivery method, brushing child's teeth every day, child number counts, and night feeding frequency in the last month were measured and included in a prediction model. Of the prediction models, the highest area under the curve of RF was 0.91 (95% CI: 0.87- 0.94), followed by 0.86 (95% CI: 0.81-0.91) of LR and 0.81 (95% CI: 0.76-0.86) of AdaBoost. ConclusionIn this CRPM, new onset of dental caries in three months among children aged < 60 months could be predicted by answering twelve nonbiological questions. A good model performance was shown within the internal validation. Dental home care could be improved by referring the CRPM result before new caries onset.

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning.

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

Nomogram prediction of caries risk among schoolchildren age 7 years based on a cohort study in Shanghai

ObjectiveCaries risk assessment tools are essential for identifying and providing treatment for individuals at high risk of developing caries. We aimed to develop a nomogram for the assessment and evaluation of caries risk among Chinese children.MethodsWe enrolled schoolchildren age 7 years from a primary school in Shanghai. Baseline information of participants was collected using a questionnaire completed by children’s caregivers. A nomogram of a novel prediction scoring model was established based on predictors detected in univariate and multivariate analyses. Predictive accuracy and discriminative ability of the nomogram were calculated using the concordance index (C index). The bootstrap method (1000 samples) was used to decrease overfitting. The net benefit of the model was validated using decision curve analysis.ResultsOverall, 406 children with complete information and two completed dental examinations were included in the final analysis. The nomogram based on logistic regression model coefficients demonstrated a C index of 0.766 (95% confidence interval: 0.761–0.771) for caries risk. The net benefit of the decision curve analysis was 38.6% at 55% threshold probability.ConclusionThis nomogram model, derived using dietary habits, oral hygiene status, and caries experience, showed promising predictive ability to assess the caries risk among Chinese children.

A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors.

Dental caries is a multifactorial disease that can be caused by interactions between genetic and environmental risk factors. Despite the availability of caries risk assessment tools, caries risk prediction models incorporating new factors, such as human genetic markers, have not yet been reported. The aim of this study was to construct a new model for caries risk prediction in teenagers, based on environmental and genetic factors, using a machine learning algorithm. We performed a prospective longitudinal study of 1,055 teenagers (710 teenagers for cohort 1 and 345 teenagers for cohort 2) aged 13 years, of whom 953 (633 teenagers for cohort 1 and 320 teenagers for cohort 2) were followed for 21 months. All participants completed an oral health questionnaire, an oral examination, biological (salivary and cariostate) tests, and single nucleotide polymorphism sequencing analysis. We constructed a caries risk prediction model based on these data using a random forest with an AUC of 0.78 in cohort 1 (training cohort). We further verified the discrimination and calibration abilities of this caries risk prediction model using cohort 2. The AUC of the caries risk prediction model in cohort 2 (testing cohort) was 0.73, indicating high discrimination ability. Risk stratification revealed that our caries risk prediction model could accurately identify individuals at high and very high caries risk but underestimated risks for individuals at low and very low caries risk. Thus, our caries risk prediction model has the potential for use as a powerful community-level tool to identify individuals at high caries risk.

Predictive assessment of clusterization of the oral mineral metabolism in mixed dentition

Relevance . The high incidence of dental caries in children remains relevant to the modern preventive dentistry. Many works have appeared in the recent years on the primary prevention of dental diseases in children based on the caries risk prediction. Thus, dental caries prognosis is relevant and requires further study to improve the methods of individual prevention in children. Purpose - to determine predictors of the oral mineral exchange in the mixed dentition according to the results of the cluster analysis. Materials and methods. A comprehensive clinical and laboratory examination of the oral metabolic processes was performed in 60 caries-resistant children with the mixed dentition aged from 7 to 12 years. Statistical analysis was performed using STATISTICA 8.0 software. Results. The study revealed strong relationship between the individual clinical and laboratory predictors, determined the optimal set of predictors for classifying patients with different types of saliva microcrystallization, which is a prerequisite for the possible caries risk prognosis in mixed dentition. Conclusions. The obtained data of the oral mineral metabolism predictors can help to plan the individual primary prevention programs in children during the active formation of the hard dental tissues and to create software to make preclinical diagnosis and prognosis of the subclinical carious process.

Improving Caries Risk Prediction Modeling: A Call for Action

Dentistry has entered an era of personalized/precision care in which targeting care to groups, individuals, or even tooth surfaces based on their caries risk has become a reality to address the skewed distribution of the disease. The best approach to determine a patient’s prognosis relies on the development of caries risk prediction models (CRPMs). A desirable model should be derived and validated to appropriately discriminate between patients who will develop disease from those who will not, and it should provide an accurate estimation of the patient’s absolute risk (i.e., calibration). However, evidence suggests there is a need to improve the methodological standards and increase consistency in the way CRPMs are developed and evaluated. In fact, although numerous caries risk assessment tools are available, most are not routinely used in practice or used to influence treatment decisions, and choice is not commonly based on high-quality evidence. Research will propose models that will become more complex, incorporating new factors with high prognostic value (e.g., human genetic markers, microbial biomarkers). Big data and predictive analytic methods will be part of the new approaches for the identification of promising predictors with the ability to monitor patients’ risk in real time. Eventually, the implementation of validated, accurate CRPMs will have to follow a user-centered design respecting the patient-clinician dynamic, with no disruption to the clinical workflow, and needs to operate at low cost. The resulting predictive risk estimate needs to be presented to the patient in an understandable way so that it triggers behavior change and effectively informs health care decision making, to ultimately improve caries outcomes. However, research on these later aspects is largely missing and increasingly needed in dentistry.

Validation of Cariogram in Caries Prediction in Women and Their Children 4 Years After Pregnancy - Longitudinal Study.

BackgroundCariogram®, an algorithm-based software model, for predicting caries risk has been used to assess the caries risk profile of many different groups. The aims of the study were to evaluate Cariogram caries risk assessment during pregnancy with DMFT/dmft incidence in mothers and their children 4 years after pregnancy and to check if there is an association between children’s caries risk profiles using Cariogram and caries risk profiles (by Cariogram) of their mothers during pregnancy.MethodsThe study population consisted of 96 pregnant women (average age 27.4±7.2 years at baseline) who completed clinical baseline examination and salivary tests. The follow-up study was initiated 4 years later and the 80 pairs of mother and children (from that pregnancy) were re-examined using the same procedure at baseline. An individual caries risk profile and DMFT/dmft incidence were made for each woman and child. The prediction of the Cariogram was compared to the actual dental experience in 4 years. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for two cut-offs were calculated to express the outcome.ResultsThe results showed a strong association between the risk categories of pregnant women and their offspring as well as between caries development in offspring and the Cariogram risk categories of pregnant women. Sensitivity and PPV for new DMFT (ΔDMFT>0) 4 years after for women were high (>80%) for those participants assessed with 0–60% “chance to avoid caries”, as well as diagnostic accuracy (74.00%). High specificity (91.00%), very high PPV (95.00%) and clinically useful values according to Youden’s index (0.53) were obtained for moderate-risk and two lowest-risk groups for dmft in children.ConclusionCariogram was valid in the authors’ sample only and highly predictive in caries risk assessment in investigated children based on caries risk assessment of their mothers in pregnancy.

Caries Risk Prediction Models in a Medical Health Care Setting

Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study was to construct and evaluate risk prediction models using information easily accessible to medical practitioners to forecast caries at 2 and 3 y of age. Data were obtained from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort. Caries was diagnosed using modified International Caries Detection and Assessment System criteria. Risk prediction models were constructed using multivariable logistic regression coupled with receiver operating characteristic analyses. Imputation was performed using multiple imputation by chained equations to assess effect of missing data. Caries rates at ages 2 y (n = 535) and 3 y (n = 721) were 17.8% and 42.9%, respectively. Risk prediction models predicting overall caries risk at 2 and 3 y demonstrated area under the curve (AUC) (95% confidence interval) of 0.81 (0.75–0.87) and 0.79 (0.74–0.84), respectively, while those predicting moderate to extensive lesions showed 0.91 (0.85–0.97) and 0.79 (0.73–0.85), respectively. Postimputation results showed reduced AUC of 0.75 (0.74–0.81) and 0.71 (0.67–0.75) at years 2 and 3, respectively, for overall caries risk, while AUC was 0.84 (0.76–0.92) and 0.75 (0.70–0.80), respectively, for moderate to extensive caries. Addition of anterior caries significantly increased AUC in all year 3 models with or without imputation (all P < 0.05). Significant predictors/protectors were identified, including ethnicity, prenatal tobacco smoke exposure, history of allergies before 12 mo, history of chronic maternal illness, maternal brushing frequency, childbearing age, and so on. Integrating oral-general health care using medical CRA models may be promising in screening caries-susceptible infants/toddlers, especially when medical professionals are trained to “lift the lip” to identify anterior caries lesions.

Can Salivary Biomarkers Be Used as Predictors of Dental Caries in Young Adolescents?

BackgroundIdentifying caries predictors in the subpopulation at risk is one of the preconditions for developing effective caries prevention measures. The present exploratory study aimed to examine the significance of socio-demographic characteristics, dietary-hygiene habits, salivary pH, and salivary antimicrobial HNP-1, hBD-2, and LL-37 peptides as potential caries risk predictors in children ages 11–13 years.Material/MethodsThis prospective 1-year study enrolled 213 children ages 11–13 years. The subjects underwent a dental examination and their mothers were interviewed. Unstimulated saliva was collected from the subjects to determine its pH value, as well as the salivary levels of HNP-1, hBD-2, and LL-37 peptides in 85 of the subjects. After 12 months, the 1-year caries incidence rate was recorded. Logistic regression analysis was used to estimate the ability of selected variables to predict caries risk.ResultsThe univariable logistic regression analysis determined that the most significant independent caries risk predictors were: sex (female) (OR=2.132, p=0.007), mothers’ education (OR=1.986, p=0.020), salivary pH (OR=0.270, p=0.043), oral hygiene index (OR=1.886, p=0.015), and daily tooth brushing frequency (OR=0.565, p=0.042). The multivariable model showed that sex and oral hygiene-related variables were the most important caries predictors.ConclusionsSalivary HNP-1, hBD-2, and LL-37 peptides were not found to have a significant predictive value. Therefore, socio-demographic and oral hygiene variables remain important caries predictors in early adolescents, suggesting the importance of the mechanical control of biofilm as the key measure for preventing caries. However, there is still a need for effective caries risk biomarkers, and additional research is needed in this area of caries risk prediction.

Success of Two Caries Risk Assessment Tools in Children: A Pilot Study With a 3-Year Follow-Up.

Caries risk is defined as the dental caries probability under the individual bacteriological environment, dietary habits, and environmental factors. Assessment of caries risk plays a major role in the prevention of dental caries. The aim of this study was to compare the effectiveness of two different caries risk assessment methods on caries-free preschool children. A sample of 90 caries-free 4- to 6-year-old children were selected from 400 preschool children. The selection was limited to children who had no dental caries, visible plaque, and systemic disease. American Academy of Pediatric Dentistry's Caries Risk Assessment Tool (CAT) and Clinpro Cario L-Pop (CCLP; 3M ESPE, Germany) were used to categorize children into caries risk groups. Children were classified as being in high-, medium-, or low-risk groups by two methods. Determining risk factors according to CAT were examined. The compliance among the two methods was evaluated. Sixty-eight children were reevaluated intraorally after 3 years. The compliance between CAT and CCLP was sought using Kappa test. Statistical evaluation revealed moderate compliance among methods at baseline. The three most determinant factors in the high-risk group according to CAT were no topical fluoride exposure, no use of dental services, and active caries in mother. CAT is found statistically more successful than CCLP in predicting caries risk 3 years later. Caries risk assessment is effective to predict future caries and can be helpful for the clinician to choose the right tailor-made caries prevention plans.

Efficacy of an adenosine triphosphate meter for evaluating caries risk in clinical dental practice

BackgroundAdenosine triphosphate bioluminescence (ATP-B) readings have been proposed as markers of caries risk. ATP readings may indicate bacteria or oral streptococci activity in microbial plaque. The authors of this study aimed to evaluate whether readings using a commercial ATP meter (CariScreen Testing Meter, Oral BioTech) are significantly different for patients with low, moderate, and high caries risk in the Caries Management by Risk Assessment Practice-Based Research Network study. MethodsTwenty practice-based research network dentists enrolled 460 patients; 271 returned for 2 or more semiannual follow-up visits over 2 years. Dentists were trained and calibrated to perform ATP-B testing and caries risk assessment (CRA) using established protocols. ATP-B readings were compared via CRA category (low, moderate, high). Generalized estimating equations were used to compare the risk of experiencing incident clinical outcomes (newly recorded decayed, missing, or restored tooth surfaces and CRA disease indicators) according to ATP-B reading at prior patient visits (≥ 1,500 versus < 1,500 relative light units). ResultsMedian ATP-B readings did not differ statistically significantly by clinician-assessed caries risk level (low, 2,323; moderate, 2,940; high, 3,217; P = .65). Adjusted for patient demographics and trial intervention assignment, higher readings were not associated with newly developed decayed, missing, or restored tooth surface (relative risk, 1.57; 95% confidence interval, 0.55 to 4.45) or disease indicators (relative risk, 1.08; 95% confidence interval, 0.85 to 1.37) at the following visit, whereas clinician-assessed caries risk level was strongly associated. ConclusionsATP-B readings poorly predicted caries risk and future clinical outcomes. CRA incorporating multiple risk, protective, and disease indicators has superior predictive performance. Practical ImplicationsThe findings of this study do not provide evidence supporting the use of ATP-B to predict caries risk.

Evaluation of CRAFT as a Tool for Caries Risk Assessment in 3- to 6-year-old Children and its Validation against Alban's Test: A Pilot Study.

ABSTRACTIntroductionDental caries is a multifactorial disease and a dynamic process that can be prevented and reversed during the early stage. Risk assessment is an important step in decision-making and treatment planning. There are no valid tools available for the Indian population. Caries risk assessment for treatment (CRAFT) is a chairside tool for caries risk assessment and management. Valid, reliable, economical, and chairside caries risk assessment tool is the need of the hour for general and pediatric dentists.AimTo evaluate CRAFT as a tool for caries risk prediction among 3-years to 6-years-old children and to validate it against Alban test.Materials and methodsA pilot study was conducted, including forty 3-years to 6-years-old children. Salivary samples were collected and inoculated on B.C.G.-Dextrose Agar. Caries activity was assessed using Alban test. Their parents/guardians completed the CRAFT assessment in entirety.ResultsThe data were tabulated and subjected to suitable statistical analysis. High positive correlation between CRAFT scores and Alban's test (Spearman's Rho = 0.874) was found.ConclusionCRAFT scores were highly correlated with Alban scores in 3- to 6-year-old children. CRAFT could be successfully employed as a reliable, economical, chairside, and clinically feasible risk assessment tool with further research on a larger sample size.Clinical significanceCRAFT can provide a framework for the clinician for preventive care and enhance patient–participation for CRA.How to cite this articleThakur JH, Subhadra HN, Jawdekar A. Evaluation of CRAFT as a Tool for Caries Risk Assessment in 3- to 6-year-old Children and its Validation against Alban's Test: A Pilot Study. Int J Clin Pediatr Dent 2019;12(6):538–542.

Oral Microbiome Studies: Potential Diagnostic and Therapeutic Implications.

Understanding the microbiology of dental caries is not a mere academic exercise; it provides the basis for preventive, diagnostic, and treatment strategies and gives the dentist a theoretical framework to become a better professional. The last years have seen the development of new research methodologies, ranging from high-throughput sequencing or "omics" techniques to new fluorescence microscopy applications and microfluidics, which have allowed the study of the oral microbiome to an unprecedented level of detail. Those studies have provided new insights about oral biofilm formation, biomarkers of caries risk, microbial etiology, appropriate sampling, identification of health-associated bacteria, and new anticaries strategies, among others. Several pitfalls are associated with the new technologies, including a small number of samples per study group, elevated cost, and genus- or species-based analyses that do not take into consideration intraspecies variability. However, the new data strongly suggest that saliva may not be an appropriate sample for etiological studies or for bacterial caries-risk tests, that microbial composition alone may be insufficient to predict caries risk, and that antimicrobial or immunization strategies targeting single species are unlikely to be effective. Strategies directed toward modulation of the oral biofilm, such as pre- and probiotics, emerge as promising new approaches to prevent tooth decay.