Development and evaluation of deep learning for screening dental caries from oral photographs

Objectives To develop and evaluate the performance of a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs. Methods 3,932 oral photographs obtained from 625 volunteers with consumer cameras were included for the development and evaluation of the model. A deep ConvNet was developed by adapting from Single Shot MultiBox Detector. The hard negative mining algorithm was applied to automatically train the model. The model was evaluated for: (i) classification accuracy for telling the existence of dental caries from a photograph and (ii) localization accuracy for locations of predicted dental caries. Results The system exhibited a classification area under the curve (AUC) of 85.65% (95% confidence interval: 82.48% to 88.71%). The model also achieved an image-wise sensitivity of 81.90%, and a box-wise sensitivity of 64.60% at a high-sensitivity operating point. The hard negative mining algorithm significantly boosted both classification (p < .001) and localization (p < .001) performance of the model by reducing false-positive predictions. Conclusions The deep learning model is promising to detect dental caries on oral photographs captured with consumer cameras. It can be useful for enabling the preliminary and cost-effective screening of dental caries among large populations.

Automated summary

This study developed and evaluated a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs. The system exhibited high classification and localization accuracy, making it promising for preliminary screening of dental caries in large populations.