Molar incisor hypomineralisation (MIH) is a qualitative developmental enamel defect with a prevalence of 13% worldwide. This review aims to outline the current evidence regarding the impact of MIH on children’s oral health and, more broadly, their day-to-day activities. MIH is associated with negative sequelae, including hypersensitivity, post-eruptive breakdown, the rapid development of carious lesions and poor aesthetics. Other concerns pertain to the clinical management of MIH and include difficulty in achieving local anaesthesia, increased dental fear and anxiety (DFA) and increased behaviour management problems. Oral health-related quality of life (OHRQoL) is the most standardised measure of patient impact; however, no instruments have been validated for use in MIH populations. The few existing observational studies investigating the impact of MIH on OHRQoL in children have produced conflicting results. Interventions to alleviate hypersensitivity and improve aesthetics had a positive impact on the OHRQoL of MIH-affected children. Multiple methodological issues make it difficult to measure the impact of MIH, including heterogeneity in the MIH severity classification, an overlap in the indices used to diagnose dental caries and MIH as well as the subjectivity of outcome measures for hypersensitivity and DFA.

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.

Causal mediation analysis is often used to explore the causal pathways linking exposures to outcomes across various research domains, including epidemiology, public health, and social sciences. It quantifies the extent to which the causal effect of an exposure on an outcome is mediated through one or more intermediate variables, known as mediators. The goal of studies conducting mediation analyses, particularly in health research, is often to understand how hypothetical interventions targeting mediators might counter exposure effects in order to guide future treatment and policy decisions as well as intervention development.