The masking effect was dramatic in some cases but not in others. The long-term colour stability of the result should be followed up through continuous clinical and scientific studies.
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 present study sought to evaluate the effect of resin shades on the degree of the polymerization. To this end, response variables affected by the degree of polymerization were examined in this study -namely, microhardness, polymerization shrinkage, and color change. Two commercial composite resins of four different shades were employed in this study: shades A3, A3.5, B3, and C3 of Z250 (Z2) and shades A3, A3.5, B3, and B4 of Solitaire 2 (S2). After light curing, the reflectance/absorbance, microhardness, polymerization shrinkage, and color change of the specimens were measured. On reflectance and absorbance, Z2 and S2 showed similar distribution curves regardless of the resin shade, with shade A3.5 of Z2 and shade A3 of S2 exhibiting the lowest/highest distributions. Similarly for attenuation coefficient and microhardness, the lowest/highest values were exhibited by shade A3.5 of Z2 and shade A3 of S2. On polymerization shrinkage, no statistically significant differences were observed among the different shades of Z2. Similarly for color change, Z2 specimens exhibited only a slight (ΔE*=0.5-0.9) color change after immersion in distilled water for 10 days, except for shades A3 and A3.5. Taken together, results of the present study suggested that the degree of polymerization of the tested composite resins was minimally affected by resin shade.
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.
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.
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