In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs. Study Design: YOLO V4, a CNN (Convolutional Neural Networks) based object detection model was used for automated tooth detection and numbering. 4545 pediatric panoramic X-ray images, processed in labelImg, were trained and tested in the Yolo algorithm. Results and Conclusions: The model was successful in detecting and numbering both primary and permanent teeth on pediatric panoramic radiographs with the mean average precision (mAP) value of 92.22 %, mean average recall (mAR) value of 94.44% and weighted-F1 score of 0.91. The proposed CNN method yielded high and fast performance for automated tooth detection and numbering on pediatric panoramic radiographs. Automatic tooth detection could help dental practitioners to save time and also use it as a pre-processing tool for detection of dental pathologies.

Purpose The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.

Objectives: This study was designed to investigate Artificial Intelligence in Dental Radiology (AIDR) videos on YouTube in terms of popularity, content, reliability, and educational quality. Methods: Two researchers systematically searched about AIDR on YouTube on January 27, 2020, by using the terms "artificial intelligence in dental radiology," "machine learning in dental radiology," and "deep learning in dental radiology." The search was performed in English, and 60 videos for each keyword were assessed. Video source, content type, time since upload, duration, and number of views, likes, and dislikes were recorded. Video popularity was reported using Video Power Index (VPI). The accuracy and reliability of the source of information were measured using the adapted DISCERN score. The quality of the videos was measured using JAMAS and modified Global Quality Score (mGQS) and content via Total Concent Evaluation (TCE). Results: There was high interobserver agreement for DISCERN (intraclass cor

Background The aim of this study was to compare the efficacy of K-type stainless steel hand instruments (Mani Inc. ), Fanta AF™ Ledge Correction (LC) (Fanta Dental), and Hyflex EDM (Coltene-Whaledent) for ledge correction, canal transport, centric ability, and shaping (preparation) time after an artificial ledge has been bypassed manually in highly curved canals using acrylic blocks. Methods Forty-two resin blocks, each with a radius of 5 mm (Endo Trainer Block, VDW) and an apical inclination of 55°, were used. Under stereomicroscope magnification, standard artificial ledges were created on acrylic blocks, and attempts were then made to eliminate them using hand instruments, FantaAF™ LC, and Hyflex EDM. Before and after images were obtained using a stereomicroscope and compared using Photoshop. Results Fanta AF™ LC and Hyflex EDM were found to be more effective for correcting ledges than hand instruments. The use of hand instruments resulted in the greatest transportation away from the canal curvature in the apical area. The canal shaping was completed in the shortest amount of time using Fanta AF™ LC, followed by HyFlex EDM and then the hand instruments. Conclusion In terms of centric ability, the order from best to worst is as follows: Fanta AF™ LC, Hyflex EDM, and hand instruments. After the ledge was manually bypassed with hand instruments in the root canals, Hyflex EDM and Fanta AF™ LC were found to be more effective than hand instruments in reshaping the previously unreachable region between the ledge and the foramen apical.