No abstract

Mesiodens caused eruption-related complications in 33.7% patients. Moreover, the risk of these complications was higher when mesiodens caused delayed development of the central incisors. These findings can aid clinicians in planning appropriate and timely treatment for mesiodens, with focus on minimising patient discomfort.

The purpose of this study is to evaluate and compare the performance of six state-of-the-art convolutional neural network (CNN)-based deep learning models for cervical vertebral maturation (CVM) on lateral cephalometric radiographs, and implement visualization of CVM classification for each model using gradient-weighted class activation map (Grad-CAM) technology. A total of 600 lateral cephalometric radiographs obtained from patients aged 6–19 years between 2013 and 2020 in Pusan National University Dental Hospital were used in this study. ResNet-18, MobileNet-v2, ResNet-50, ResNet-101, Inception-v3, and Inception-ResNet-v2 were tested to determine the optimal pre-trained network architecture. Multi-class classification metrics, accuracy, recall, precision, F1-score, and area under the curve (AUC) values from the receiver operating characteristic (ROC) curve were used to evaluate the performance of the models. All deep learning models demonstrated more than 90% accuracy, with Inception-ResNet-v2 performing the best, relatively. In addition, visualizing each deep learning model using Grad-CAM led to a primary focus on the cervical vertebrae and surrounding structures. The use of these deep learning models in clinical practice will facilitate dental practitioners in making accurate diagnoses and treatment plans.

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3–V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6–12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

In this study, we aimed to develop and evaluate the performance of deep-learning models that automatically classify mesiodens in primary or mixed dentition panoramic radiographs. Panoramic radiographs of 550 patients with mesiodens and 550 patients without mesiodens were used. Primary or mixed dentition patients were included. SqueezeNet, ResNet-18, ResNet-101, and Inception-ResNet-V2 were each used to create deep-learning models. The accuracy, precision, recall, and F1 score of ResNet-101 and Inception-ResNet-V2 were higher than 90%. SqueezeNet exhibited relatively inferior results. In addition, we attempted to visualize the models using a class activation map. In images with mesiodens, the deep-learning models focused on the actual locations of the mesiodens in many cases. Deep-learning technologies may help clinicians with insufficient clinical experience in more accurate and faster diagnosis.

BackgroundMedication reviews have become part of pharmacy practice across developed countries. This study aimed to identify factors affecting the likelihood of eligible Ontario seniors receiving a pharmacy-led medication review called MedsCheck annual (MCA).MethodsWe designed a cohort study using a random sample of pharmacy claims for MCA-eligible Ontario seniors using linked administrative data from April 2012 to March 2013. Guided by a conceptual framework, we constructed a generalized-estimating-equations model to estimate the effect of patient, pharmacy and community factors on the likelihood of receiving MCA.ResultsOf the 2,878,958 eligible claim-dates, 65,605 included an MCA. Compared to eligible individuals who did not receive an MCA, recipients were more likely to have a prior MCA (OR = 3.03), receive a new medication on the claim-date (OR = 1.78), be hypertensive (OR = 1.18) or have a recent hospitalization (OR = 1.07). MCA recipients had fewer medications (e.g., OR = 0.44 for ≥12 medications versus 0–4 medications), and were less likely to receive an MCA in a rural (OR = 0.74) or high-volume pharmacy (OR = 0.65).ConclusionsThe most important determinant of receiving an MCA was having had a prior MCA. Overall, MCA recipients were healthier, younger, urban-dwelling, and taking fewer medications than non-recipients. Policies regarding current and future medication review programs may need to evolve to ensure that those at greatest need receive timely and comprehensive medication reviews.

Background The COVID-19 pandemic changed the world and created a shift in the dental education program. This sudden change in the dental education program may have affected the academic standards of dental students. This study aimed to evaluate the overall satisfaction and effectiveness of online learning in pediatric dentistry of undergraduate dental students’ during the COVID-19 pandemic in South Korea. Methods An anonymous online survey was sent to three dental schools, and responses were collected from dental school students. Questions included the demographics, perspectives of online classes, comparison of online and offline pediatric dentistry classes and opinions on how dental schools are handling the pandemic. Students’ perspectives on online classes were evaluated based on satisfaction with online education. Data were analyzed using the Kruskal-Wallis test and the Mann-Whitney U test. Results Most students took online classes from home (80.9%) using Zoom (50.4%). The majority reported overall program satisfaction (74.1%) and agreed that universities implemented online classes well (55%). Students who were in favor of online classes responded more positively to questions on the effectiveness and safety of online learning (p < 0.05). Regardless of satisfaction with online education, the students agreed that the online education shift was the right decision in pandemic outbreak. Conclusions Dental students in South Korea preferred and adapted well to the web-based learning program in pediatric dentistry during COVID-19 pandemic.

The aim of the study was to evaluate the effects of polymeric computer-aided design/computer-aided manufacturing CAD/CAM materials on antagonistic primary tooth wear. Five CAD/CAM polymeric materials were examined: Vipi Block Monocolor (VBM), Yamahachi polymethylmethacrylate (PMMA) (YAP), Mazic Duro (MZD), Vita Enamic (ENA), and Pekkton (PEK). All of the specimens were tested in a thermomechanical loading machine with the primary canine as the antagonist (50 N, 1.2 × 105 cycles, 1.7 Hz, 5/55 °C). The wear losses of the antagonist tooth and the restorative materials were calculated using reverse modelling software and an electronic scale. VBM and ENA showed significantly higher antagonist tooth wear than PEK (p < 0.05), but there was no significant difference observed among VBM, YAP, MZD, and ENA (p > 0.05). PEK showed the largest value in both material volumetric and weight losses. In terms of material volumetric losses, there was no significant difference between all of the groups (p > 0.05). In terms of material weight losses, PEK was significantly larger than ENA (p < 0.05), but there was no significant difference between VBM, YAP, MZD, and ENA (p > 0.05). Volumetric and weight losses of materials showed similar wear behaviour. However, the wear patterns of antagonists and materials were different, especially in PEK.