Accurate classification of pediatric dental diseases from panoramic radiographs is crucial for early diagnosis and treatment planning. This study explores a text-based approach using a natural language transformer to generate textual descriptions of radiographs, which are then classified using deep learning models. Three models were evaluated: 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 for binary disease classification. Results showed that BERT achieved 77% accuracy, excelling in detecting periapical infections but struggling with caries identification. The 1D-CNN outperformed BERT with 84% accuracy, providing a more balanced classification, while the LSTM model achieved only 57% accuracy. Both 1D-CNN and BERT surpassed three pretrained CNN models trained directly on panoramic radiographs, indicating that text-based classification is a viable alternative to traditional image-based methods. These findings highlight the potential of language-based models for radiographic interpretation while underscoring challenges in generalizability. Future research should refine text generation, develop hybrid models integrating textual and image-based features, and validate performance on larger datasets to enhance clinical applicability.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

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

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes