Deep Learning for Diagnostic Charting on Pediatric Panoramic Radiographs.

Artificial intelligence (AI) based systems are used in dentistry to make the diagnostic process more accurate and efficient. The objective of this study was to evaluate the performance of a deep learning program for detection and classification of dental structures and treatments on panoramic radiographs of pediatric patients. In total, 4821 anonymized panoramic radiographs of children aged between 5 and 13 years old were analyzed by YOLO V4, a CNN (Convolutional Neural Networks) based object detection model. The ability to make a correct diagnosis was tested samples from pediatric patients examined within the scope of the study. All statistical analyses were performed using SPSS 26.0 (IBM, Chicago, IL, USA). The YOLOV4 model diagnosed the immature teeth, permanent tooth germs and brackets successfully with the high F1 scores like 0.95, 0.90 and 0.76 respectively. Although this model achieved promising results, there were certain limitations for some dental structures and treatments including the filling, root canal treatment, supernumerary tooth. Our architecture achieved reliable results with some specific limitations for detecting dental structures and treatments. Detection of certain dental structures and previous dental treatments on pediatric panoramic x-rays by using a deep learning-based approach may provide early diagnosis of some dental anomalies and help dental practitioners to find more accurate treatment options by saving time and effort.