This study proposes a deep learning vision-language model for the automated diagnosis of pediatric dental diseases, with a focus on differentiating between caries and periapical infections. The model integrates visual features extracted from panoramic radiographs using methods of non-linear dynamics and textural encoding with textual descriptions generated by a large language model. These multimodal features are concatenated and used to train a 1D-CNN classifier. Experimental results demonstrate that the proposed model outperforms conventional convolutional neural networks and standalone language-based approaches, achieving high accuracy (90%), sensitivity (92%), precision (92%), and an AUC of 0.96. This work highlights the value of combining structured visual and textual representations in improving diagnostic accuracy and interpretability in dental radiology. The approach offers a promising direction for the development of context-aware, AI-assisted diagnostic tools in pediatric dental care.

The authors have declared no competing interest.

This study did not receive any funding.

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The study used ONLY openly available human data that were originally located at: https://doi.org/10.6084/m9.figshare.c.6317013.v1

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