Annotation-free deep learning approach for detection of periapical bone rarefactions on panoramic radiographs: a feasibility study.

OBJECTIVE

To evaluate an annotation-free deep learning (DL) approach for the image-level detection of periapical bone rarefactions (PBRs) on panoramic radiographs (PRs).

STUDY

DESIGN

A retrospective dataset of 2,527 PRs was classified at the image level based on expert consensus using internationally accepted radiographic criteria. Multiple pretrained convolutional neural network (CNN) and vision transformer (ViT) architectures were evaluated under different modeling strategies, including fine-tuning and attention mechanisms. Model performance was assessed using accuracy, recall, specificity, and F1 score. Inferential comparisons were performed using nonparametric statistical tests.

RESULTS

CNN-based models consistently outperformed ViT architectures in binary classification of PBRs. The fine-tuned Xception model achieved the highest overall performance, with an F1-score of 64.7% and low variability across repeated runs. Analyses were restricted to Classes A and B due to class imbalance constraints, then multiclass evaluation was not performed at this stage.

CONCLUSIONS

Annotation-free DL demonstrated feasibility for binary image-level detection of PBRs on PRs, reducing dependence on labor-intensive spatial annotations while maintaining clinically interpretable behavior. Multiclass classification remains a target for investigation with balanced datasets. The approach supports scalable image-level analysis under clinically realistic conditions, although external validation is still required to confirm generalizability.