Background

Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment are desirable in children with supernumerary teeth.

Aim

This study aimed to apply convolutional neural network (CNN)-based deep learning to detecting the presence of supernumerary teeth in children during the early mixed dentition stage.

Design

Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analysed. The CNNs performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristics (ROC) curves, and area under the ROC curves for a cross-validation test dataset.

Results

The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models showed similar performance.

Conclusion

CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.