The authors undertook an updated systematic review of the relationship between body mass index and dental caries in children and adolescents. The authors searched Medline, ISI, Cochrane, Scopus, Global Health and CINAHL databases and conducted lateral searches from reference lists for papers published from 2004 to 2011, inclusive. All empirical papers that tested associations between body mass index and dental caries in child and adolescent populations (aged 0 to 18 years) were included. Dental caries is associated with both high and low body mass index. A non-linear association between body mass index and dental caries may account for inconsistent findings in previous research. We recommend future research investigate the nature of the association between body mass index and dental caries in samples that include a full range of body mass index scores, and explore how factors such as socioeconomic status mediate the association between body mass index and dental caries.

This study aimed to demonstrate the use of deep learning for automating caries detection using intraoral scan data from children and to evaluate diagnostic agreement between the models' predictions and dental practitioner assessments on 3D models. Intraoral scans were collected from two cohorts at Murdoch Children's Research Institute. Two researchers annotated scan meshes using the International Caries Classification and Management System. A pre-processing pipeline converted the data into a 2D format. Carious teeth from the first cohort (n = 332) were split at the participant level into training (n = 192), validation (n = 63), and test (n = 77) sets. An Attention U-Net was trained to classify initial, moderate, and extensive dental caries. Segmentation and lesion detection performance was evaluated on the test set using the metrics Intersection over Union (IoU), Sensitivity (SE), Specificity (SP), and Precision (P). Carious teeth from the second independent cohort (n = 119) were used for external validation. Multilevel logistic regression assessed diagnostic agreement to compare the model performance to dental practitioners across all caries thresholds (initial, moderate and extensive). For segmentation tasks, the model had the best performance for extensive caries (SE 71 %, P 66 %, IOU 0.55). The model showed overall promising performance for lesion detection (SE 67 %, P 73 %). Performance slightly declined on an external dataset. Diagnostic agreement between the model and dental practitioners was comparable across all disease thresholds: initial (odds ratio OR 0.82, 95 % Confidence Interval (CI) 0.6-1.15), moderate (OR 0.9, 95 % CI 0.5-1.6) and extensive (OR 0.85, 95 % CI 0.42-1.71). The proof-of-concept demonstrates that deep learning can achieve moderate performance in detecting extensive caries from intraoral scans, though performance was limited for early and moderate lesions. Further research is needed to improve model accuracy and generalisability across all disease stages. This study represents an exploratory effort towards developing AI-assisted caries detection using intraoral scanner data in children. While the long-term potential of such technology could include support for early diagnosis, enhanced caries monitoring, and a reduction in the subjectivity of caries assessment, our current findings indicate that significant model refinement and extensive validation are imperative, especially for the detection of initial carious lesions, before such clinical applications can be realized.