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.

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The purpose of this retrospective study was to evaluate the survival rate by comparing Class II restoration using flowable resin composite with stainless steel crown in primary molars.Electronic medical records and radiographs of 1,504 primary molars with proximal caries of 590 patients from June 2015 to August 2019 were analyzed. With the collected data, survival analysis was performed using the Kaplan-Meier method.The 1-year survival rate of flowable resin composite in the primary molar was 98.5%, 3-year survival rate was 87.7%, and mean survival time was 39 months. There was no statistically significant difference between flowable resin composite and stainless steel crown (<i>p</i> = 0.896).Within the limits of this study, Class II restoration using flowable resin composite can be considered a promising option for the treatment of proximal caries in primary molars.

The purpose of this study was to measure bone age using an artificial intelligence program based on the Greulich-Pyle (GP) method to find out the bone age corresponding to each stage of cervical vertebral maturation (CVM) and the middle phalanx of the third finger (MP3). This study was conducted on 3,118 patients who visited pediatric dentistry at Kyung Hee University Dental Hospital and Pusan National University Dental Hospital from 2013 to 2021. The CVM stage was divided into 5 stages according to the classification by Baccetti, and the MP3 stage was divided into 5 stages according to the methods of Hägg and Taranger. Based on the GP method, bone age was evaluated using an artificial intelligence program. The pubertal growth spurt in the CVM stage was CVM II and III. The mean bone age in CVM II was 11.00 ± 1.81 years for males and 10.00 ± 1.49 years for females, and in CVM III, 13.00 ± 1.46 years for males and 12.00 ± 1.44 years for females (<i>p</i> < 0.0001). The pubertal growth spurt in the MP3 stage was MP3 - G stage. The bone age at the MP3 - G stage was 13.14 ± 1.07 years for males and 11.40 ± 1.09 years for females (<i>p</i> < 0.0001). Bone age evaluation using artificial intelligence is worth using in clinical practice, and it is expected that a faster and more accurate diagnosis will be possible.