Classification of pediatric dental diseases from panoramic radiographs using natural language transformer and deep learning models

Introduction Accurate classification of pediatric dental diseases from panoramic radiographs is essential for early diagnosis and effective treatment planning. While deep learning models traditionally operate directly on image data, text-based representations generated from radiographs may provide an alternative strategy for disease classification. Methods This study proposed a text-driven framework in which a natural language transformer was used to generate structured textual descriptions from panoramic radiographs. These descriptions were subsequently classified for binary disease detection using three deep learning architectures: a one-dimensional convolutional neural network (1D-CNN), a long short-term memory (LSTM) network, and a pretrained Bidirectional Encoder Representations from Transformer (BERT) model. Model performance was evaluated and compared against three pretrained convolutional neural networks trained directly on radiographic images. Results The 1D-CNN achieved the highest performance with 84% accuracy, demonstrating balanced classification across disease categories. The BERT model reached 77% accuracy, showing strong performance in detecting periapical infections but comparatively lower sensitivity for caries identification. The LSTM model performed substantially worse, achieving 57% accuracy. Both the 1D-CNN and BERT text-based approaches outperformed the three image-based pretrained CNN models. Discussion These findings suggest that text-based classification of panoramic radiographs is a potential alternative to conventional image-based deep learning methods. Language-driven models show promise for radiographic interpretation; however, challenges remain in achieving consistent generalizability across disease types. Future research should focus on improving radiograph-to-text generation quality, developing hybrid architectures that integrate textual and visual features, and validating performance on larger and more diverse datasets to strengthen clinical applicability.