Deep Learning for Dental Problems and Their Detection: DentalAI

Oral diseases such as dental caries, calculus, gingivitis, and mouth ulcers are among the most commonly occurring health conditions worldwide and often remain undiagnosed in their early stages due to lack of awareness and limited access to dental care. Early detection of these diseases is essential to prevent serious complications and improve overall oral health. In this study, an automated dental disease detection system named DentalAI is proposed using deep learning for the classification of common oral diseases from dental images. The system is developed using the ResNet34 convolutional neural network architecture with the help of transfer learning to improve learning efficiency and accuracy. A publicly available oral disease image dataset was used for model training and evaluation. The dataset was carefully preprocessed and enhanced using image augmentation techniques such as rotation, flipping, zooming, and brightness adjustment to improve generalization and reduce overfitting. The model was trained to classify four disease categories: calculus, dental caries, gingivitis, and mouth ulcers. Experimental evaluation was carried out on a test set consisting of 620 dental images. The proposed model achieved an overall classification accuracy of 80.48%. Class-wise performance analysis showed strong detection capability for gingivitis and mouth ulcers, while moderate confusion was observed between clinically similar conditions such as calculus and gingivitis. The confusion matrix and performance metrics confirmed that the system learned meaningful disease-specific visual features and demonstrated stable predictive behavior across all classes. The results indicate that the proposed system can serve as an effective AI-assisted preliminary dental screening tool, especially in rural and underserved areas where access to dental specialists is limited. Although the system is not intended to replace professional dental diagnosis, it provides a fast, low-cost, and accessible solution for early-stage oral disease detection. With further improvements in dataset size, class balance, and model optimization, the performance of the system can be enhanced further. This work highlights the strong potential of deep learning techniques in the development of smart and accessible dental healthcare systems.