  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.
