The association between molar incisor hypomineralization and oral health-related quality of life: a cross-sectional study

Objectives

We aimed to assess the association between molar incisor hypomineralization (MIH) and the oral health-related quality of life (OHRQoL) in a group of 7- to 14-year-old children in Berlin, Germany.

Materials and methods

The cross-sectional study consisted of a consecutive sample of 317 children, aged 7–14 years (49% girls, 51% boys; mean age, 8.71). Data were collected between June 2018 and December 2019. MIH was diagnosed using the criteria of the European Academy of Paediatric Dentistry. OHRQoL was assessed using the German 19-item version of the Child Oral Health Impact Profile (COHIP-G19). Differences in COHIP-19 summary scores between controls without MIH and MIH patients and with regards to MIH severity were tested for statistical significance using t test and analysis of variance (ANOVA), respectively.

Results

Data were obtained for 217 untreated MIH patients and 100 controls. OHRQoL of MIH patients was significantly more impaired than of controls indicated by COHIP-19 mean scores (60.9 ± 10.7 vs. 67.9 ± 7.8; p < 0.001). Patients with severe MIH (59.6 ± 11.0) reported significantly worse OHRQoL than patients with mild MIH (63.6 ± 9.1; p = 0.013).

Conclusions

MIH has a significant negative impact on the children’s OHRQoL. Patients with severe MIH experience a greater negative impact on OHRQoL than those diagnosed with mild MIH.

Clinical significance

MIH is one of the major dental problems of our time; pediatric dentists should be aware of its impact on the OHRQoL of the patient.

Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection

Artificial intelligence (AI) can assist dentists in image assessment, for example, caries detection. The wider health and cost impact of employing AI for dental diagnostics has not yet been evaluated. We compared the cost-effectiveness of proximal caries detection on bitewing radiographs with versus without AI. U-Net, a fully convolutional neural network, had been trained, validated, and tested on 3,293, 252, and 141 bitewing radiographs, respectively, on which 4 experienced dentists had marked carious lesions (reference test). Lesions were stratified for initial lesions (E1/E2/D1, presumed noncavitated, receiving caries infiltration if detected) and advanced lesions (D2/D3, presumed cavitated, receiving restorative care if detected). A Markov model was used to simulate the consequences of true- and false-positive and true- and false-negative detections, as well as the subsequent decisions over the lifetime of patients. A German mixed-payers perspective was adopted. Our health outcome was tooth retention years. Costs were measured in 2020 euro. Monte-Carlo microsimulations and univariate and probabilistic sensitivity analyses were conducted. The incremental cost-effectiveness ratio (ICER) and the cost-effectiveness acceptability at different willingness-to-pay thresholds were quantified. AI showed an accuracy of 0.80; dentists’ mean accuracy was significantly lower at 0.71 (minimum–maximum: 0.61–0.78, P < 0.05). AI was significantly more sensitive than dentists (0.75 vs. 0.36 [0.19–0.65]; P = 0.006), while its specificity was not significantly lower (0.83 vs. 0.91 [0.69–0.98]; P > 0.05). In the base-case scenario, AI was more effective (tooth retention for a mean 64 [2.5%–97.5%: 61–65] y) and less costly (298 [244–367] euro) than assessment without AI (62 [59–64] y; 322 [257–394] euro). The ICER was −13.9 euro/y (i.e., AI saved money at higher effectiveness). In the majority (>77%) of all cases, AI was less costly and more effective. Applying AI for caries detection is likely to be cost-effective, mainly as fewer lesions remain undetected. Notably, this cost-effectiveness requires dentists to manage detected early lesions nonrestoratively.