Influence of contact points on the performance of caries detection methods in approximal surfaces of primary molars: an in vivo study.

This in vivo study aimed to evaluate the influence of contact points on the approximal caries detection in primary molars, by comparing the performance of the DIAGNOdent pen and visual-tactile examination after tooth separation to bitewing radiography (BW). A total of 112 children were examined and 33 children were selected. In three periods (a, b, and c), 209 approximal surfaces were examined: (a) examiner 1 performed visual-tactile examination using the Nyvad criteria (EX1); examiner 2 used DIAGNOdent pen (LF1) and took BW; (b) 1 week later, after tooth separation, examiner 1 performed the second visual-tactile examination (EX2) and examiner 2 used DIAGNOdent again (LF2); (c) after tooth exfoliation, surfaces were directly examined using DIAGNOdent (LF3). Teeth were examined by computed microtomography as a reference standard. Analyses were based on diagnostic thresholds: D1: D 0 = health, D 1 ­D 4 = disease; D2: D 0 , D 1 = health, D 2 ­D 4 = disease; D3: D 0 ­D 2 = health, D 3 , D 4 = disease. At D1, the highest sensitivity/specificity were observed for EX1 (1.00)/LF3 (0.68), respectively. At D2, the highest sensitivity/ specificity were observed for LF3 (0.69)/BW (1.00), respectively. At D3, the highest sensitivity/specificity were observed for LF3 (0.78)/EX1, EX2 and BW (1.00). EX1 showed higher accuracy values than LF1, and EX2 showed similar values to LF2. We concluded that the visual-tactile examination showed better results in detecting sound surfaces and approximal caries lesions without tooth separation. However, the effectiveness of approximal caries lesion detection of both methods was increased by the absence of contact points. Therefore, regardless of the method of detection, orthodontic separating elastics should be used as a complementary tool for the diagnosis of approximal noncavitated lesions in primary molars.

An Automated Machine Learning Classifier for Early Childhood Caries.

Purpose: The purpose of the study was to develop and evaluate an automated machine learning algorithm (AutoML) for children's classification according to early childhood caries (ECC) status. Methods: Clinical, demographic, behavioral, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used. ECC prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication. Results: A parsimonious model including two terms (i.e., children's age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviors, fluoride exposure, and dental home had worse performance (AUC equals 0.66, Se equals 0.54, PPV equals 0.61). Conclusions: Parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for ECC screening. The classifier can accommodate biological information that can help improve its performance in the future.

The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

Early childhood caries (ECC) is a biofilm-mediated disease. Social, environmental, and behavioral determinants as well as innate susceptibility are major influences on its incidence; however, from a pathogenetic standpoint, the disease is defined and driven by oral dysbiosis. In other words, the disease occurs when the natural equilibrium between the host and its oral microbiome shifts toward states that promote demineralization at the biofilm-tooth surface interface. Thus, a comprehensive understanding of dental caries as a disease requires the characterization of both the composition and the function or metabolic activity of the supragingival biofilm according to well-defined clinical statuses. However, taxonomic and functional information of the supragingival biofilm is rarely available in clinical cohorts, and its collection presents unique challenges among very young children. This paper presents a protocol and pipelines available for the conduct of supragingival biofilm microbiome studies among children in the primary dentition, that has been designed in the context of a large-scale population-based genetic epidemiologic study of ECC. The protocol is being developed for the collection of two supragingival biofilm samples from the maxillary primary dentition, enabling downstream taxonomic (e.g., metagenomics) and functional (e.g., transcriptomics and metabolomics) analyses. The protocol is being implemented in the assembly of a pediatric precision medicine cohort comprising over 6000 participants to date, contributing social, environmental, behavioral, clinical, and biological data informing ECC and other oral health outcomes.