Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.

Abstract

Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth. This study aimed to apply convolutional neural network (CNN)-based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage. Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6years 0months to 9years 6months) including supernumerary teeth (cases, n=120) or no anomalies (controls, n=100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset. The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance. CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.