Detection and Numbering of Primary Teeth in Panoramic Radiographs with Deep Learning Method

Background: Artificial intelligence (AI) has made significant contributions to numerous technological advancements, particularly in radiological diagnostics, and continues to demonstrate progressive development within the domain of dentistry. This study aims to assess the efficacy of deep learning algorithms in the detection and numbering of primary dentition through panoramic radiographic analysis. Methods: This study incorporated a total of 701 panoramic radiographs obtained from pediatric individuals aged 5-9 years. The methodology employed a convolutional neural network (CNN)- based framework, specifically utilizing the mask region based (R)-CNN architecture, for automated dental detection and enumeration. The model’s performance metrics were systematically evaluated through confusion matrix analysis. Results: The model demonstrated superior performance metrics in the detection and numbering of deciduous dentition within panoramic radiographic analyses. The precision, recall, and F1-score values calculated using the confusion matrix were 0.973, 0.947, and 0.960 for the age group 5, 0.944, 0.904, and 0.923 for the age group 6, 0.959, 0.932, and 0.945 for the age group 7, 0.984, 0.913, and 0.947 for the age group 8, 0.920, 0.904, and 0.912 for the age group 9, respectively. Conclusion: Deep learning–based AI models are a promising approach for automatic detection and numbering of deciduous teeth in panoramic radiographs from children.