An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images

Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people's quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries-non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification.

Automated summary

Dental caries is a common dental health issue that can cause pain and infections, reducing quality of life. Applying machine learning models for caries detection can lead to early treatment, but lack of explainability may hinder their acceptance. In this study, an explainable deep learning model for detecting dental caries was developed and evaluated. The ResNet-50 model showed slightly better performance compared to EfficientNet-B0 and DenseNet-121, achieving an accuracy of 92.00% and a sensitivity of 87.33%. The heat maps provided by the model helped explain the classification results, enabling dentists to validate and reduce misclassification.