Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation

TL;DR

This paper introduces a semi-supervised framework combining large language models, autoencoders, and vision transformers to classify dental conditions in panoramic radiographs, effectively leveraging unlabeled data and large datasets.

Contribution

It presents a novel semi-supervised approach integrating language models and deep learning techniques for dental radiograph classification, addressing data scarcity issues.

Findings

Achieved baseline or superior Matthews correlation coefficients.

Performance comparable to junior dental specialists.

Validated on large, real-world datasets.

Abstract

Dental panoramic radiographs offer vast diagnostic opportunities, but training supervised deep learning networks for automatic analysis of those radiology images is hampered by a shortage of labeled data. Here, a different perspective on this problem is introduced. A semi-supervised learning framework is proposed to classify thirteen dental conditions on panoramic radiographs, with a particular emphasis on teeth. Large language models were explored to annotate the most common dental conditions based on dental reports. Additionally, a masked autoencoder was employed to pre-train the classification neural network, and a Vision Transformer was used to leverage the unlabeled data. The analyses were validated using two of the most extensive datasets in the literature, comprising 8,795 panoramic radiographs and 8,029 paired reports and images. Encouragingly, the results consistently met or surpassed the baseline metrics for the Matthews correlation coefficient. A comparison of the proposed solution with human practitioners, supported by statistical analysis, highlighted its effectiveness and performance limitations; based on the degree of agreement among specialists, the solution demonstrated an accuracy level comparable to that of a junior specialist.