Pediatric tooth numbering
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Pediatric Tooth Numbering: Insights from Recent Research
Deep Learning Models for Pediatric Tooth Numbering
Identification of Tooth Number Abnormalities Using Deep Learning
Recent advancements in deep learning have significantly improved the identification of tooth number abnormalities in pediatric patients. A study utilized a deep learning model with ResNet-50 as the backbone network to analyze 1,707 panoramic radiographs of children aged 4 to 11 years. The model demonstrated high sensitivity (97.1%-98.0%) and specificity (98.3%-98.4%) in detecting abnormalities such as extra teeth and missing permanent teeth. The Kappa values, indicating agreement between the model and human experts, were 0.886 for the internal test set and 0.912 for the external test set, showcasing the model's reliability in clinical settings.
CNN-Based Automated Tooth Detection and Numbering
Another study proposed the use of YOLO V4, a Convolutional Neural Network (CNN) based object detection model, for automated tooth detection and numbering on pediatric panoramic radiographs. The model was trained on 4,545 pediatric X-ray images and achieved a mean average precision (mAP) of 92.22% and a mean average recall (mAR) of 94.44%. This high performance suggests that CNN-based methods can effectively and quickly assist dental practitioners in identifying and numbering both primary and permanent teeth, potentially saving time and aiding in the detection of dental pathologies.
AI Systems for Deciduous Tooth Detection
A study focused on the use of an AI algorithm, specifically the Faster R-CNN Inception v2 model, for the detection and numbering of deciduous teeth in children. The system was trained on 421 panoramic images and showed high sensitivity (0.9804) and precision (0.9571), with an F1 score of 0.9686. These results indicate that AI models are promising tools for the automated charting of pediatric dental radiographs, providing valuable support in clinical and forensic settings.
Prevalence and Characteristics of Supernumerary Teeth
Retrospective Studies on Supernumerary Teeth
Supernumerary teeth, an anomaly where extra teeth are present, have been studied extensively to understand their prevalence and characteristics. A retrospective study reviewed medical records and panoramic radiographs of 3,127 pediatric patients, finding a 2.8% prevalence of supernumerary teeth, with a higher occurrence in males (56.3%). Most supernumerary teeth were conical (65.3%) and located palatally (78.2%), with 63.4% being erupted.
Investigation of Tooth Number and Morphology Abnormalities
Another study analyzed 4,347 panoramic radiographs of children aged 5 to 15 years to investigate tooth number and morphology abnormalities. The prevalence of these abnormalities was found to be 31.79%, with tooth agenesis (15.00%) and supernumerary teeth (12.61%) being the most common. The study highlighted the importance of early diagnosis through panoramic radiographs to assist pediatric dentists in formulating appropriate treatment plans.
Demographic Profile of Supernumerary Teeth in Brazilian Children
A survey conducted on Brazilian children and adolescents identified 460 supernumerary teeth in 305 patients. The majority of these teeth were single (63.0%), conical (44.6%), and unerupted (76.8%). The study emphasized the need for early detection and management of supernumerary teeth to prevent complications such as permanent teeth displacement.
Conclusion
The integration of deep learning and AI in pediatric dentistry has shown promising results in the automated detection and numbering of teeth, significantly aiding in the diagnosis and management of dental anomalies. Studies on the prevalence and characteristics of supernumerary teeth further underscore the importance of early detection and intervention to ensure optimal oral health outcomes for pediatric patients.
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