Social Economic Disadvantage and Untreated Dental Caries: Findings from a Cohort Study in Adolescents.

This study aimed to assess the impact of determinants of the individual and contextual level on the untreated dental caries during adolescence. A cohort study was started in 2012 with a random sample of 1,134 12-year-old adolescents in the city of Santa Maria, RS, Brazil. The adolescents were clinically evaluated by calibrated dentists and investigated variables: contextual, demographic, socioeconomic factors, dental service use, toothache, and subjective variables. After 2 years (T2) and 6 years (T3), the same adolescents were reevaluated (retention rate of 67.9% and 67.8%, respectively). Untreated dental caries (component "D" of the DMFT index) was the outcome and was collected at all three times. Multilevel Poisson regression analyses considered repeated measures (level 1 - times), nested to adolescents (level 2), were used to assess the association between predictors (baseline) and untreated dental caries. High neighborhood's mean income was associated with the lowest risk of dental caries. Low household income (incidence rate ratio [IRR] 1.57; confidence interval 95% [CI]: 1.35-1.82), low mother education (IRR 1.19; 95% CI: 1.03-1.38), toothache (IRR 1.73; 95% CI: 1.47-2.03), and poor self-perception of oral health (IRR 1.19; 95% CI: 1.07-1.32) were risk factors for untreated dental caries. In conclusion, our results showed that socioeconomic disadvantages and oral conditions in early adolescence are risk factors for untreated caries among adolescents.

Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review.

We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Scopus, EMBASE, Web of Science, and grey literature until December 2020. We excluded review articles, case series, case reports, editorials, letters, comments, educational methodologies, assessments of robotic devices, and articles with less than 10 participants or specimens. Two independent reviewers selected the studies and performed the assessment of the methodological quality based on standardized scales. We summarize data on the machine learning algorithms used; software; performance outcomes such as accuracy/precision, sensitivity/recall, specificity, area under the receiver operating characteristic curve (AUC), and positive/negative predictive values related to dental caries. Meta-analyses were not performed due to methodological differences. Our review included 15 studies (10 diagnostic studies and 5 prognostic prediction studies). Cross-sectional design studies were predominant (12). The most frequently used statistical measure of performance reported in diagnostic studies was AUC value, which ranged from 0.745 to 0.987. For most diagnostic studies, data from contingency tables were not available. Reported sensitivities were higher in low risk of bias prognostic prediction studies (median [IQR] of 0.996 [0.971-1.000] vs. unclear/high risk of bias studies 0.189 [0-0.340]; p value 0.025). While there were no significant differences in the specificity between these subgroups, we concluded that the use of these technologies for the diagnosis and prognostic prediction of dental caries, although promising, is at an early stage. The general applicability of the evidence was limited given that most models were developed outside the real clinical setting with a prevalence of unclear/high risk of bias. Researchers must increase the overall quality of their research protocols by providing a comprehensive report on the methods implemented.

Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review.

BACKGROUND: Machine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures. AIM: The aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice. RELEVANCE FOR PATIENTS: The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.

Pathways influencing dental caries increment among children: A cohort study.

BACKGROUND: Dentists should assess pathways influencing the increment of dental caries among children to guide the prevention and treatment of the disease. AIM: Evaluate the pathways that influence the increment of carious lesions in pre-school children. DESIGN: This is a 2-year cohort study was conducted with a random sample of 639 pre-school children in southern Brazil. Caries experience, socioeconomic status (SES), social capital, and psychosocial characteristics were obtained at baseline. Increment of dental caries was assessed at 2 years follow-up in 467 children (cohort retention rate of 73.1%). Previously calibrated examiners assess the caries through the International Caries Detection and Assessment System (ICDAS). Structural equation modeling (SEM) was performed to test the pathways influencing dental caries increment. RESULTS: Dental caries at baseline was heavily influenced by children's age (SC: 0.381, P < .01), tooth plaque (SC: 0.077, P = .02), parent's perception child oral health (SC: 0.295, P < .01), and household (SC: 0.148, P < .01). Increment of dental caries was directly affected by dental caries at baseline (Standardized Coefficients [SC]: 0.377, P < .01). Indirect paths were not significant. CONCLUSIONS: Dental caries experience was the main factor of direct influence on the increment of caries, reinforcing the theory of risk accumulation over time.