Deep learning for early dental caries detection in bitewing radiographs

The early detection of initial dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior initial caries. In medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched, with promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for caries detection on bitewing radiographs and investigated whether this model can improve clinicians' performance. The research complied with relevant ethical regulations. In total, 304 bitewing radiographs were used to train the CNN model and 50 radiographs for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased sensitivity ratio (D1, 85.34%; D1 ', 92.15%; D2, 85.86%; D2 ', 93.72%; D3, 69.11%; D3 ', 79.06%; p < 0.05). These increases were especially significant (p < 0.05) in the initial and moderate caries subgroups. The deep learning model may help clinicians to diagnose dental caries more accurately.

Automated summary

The study developed a CNN model using U-Net for caries detection on bitewing radiographs, and demonstrated that the model can help clinicians diagnose caries more accurately, especially in cases of initial and moderate caries, as shown by the improved diagnostic performance of three dentists using the model's results.