Advancements in Caries Diagnostics Using Bitewing Radiography: A Systematic Review of Deep Learning Approaches.

INTRODUCTION

Deep learning techniques have emerged as promising tools for enhancing the radiographic diagnosis of caries, particularly when utilizing bitewing radiographs.

METHODS

Following the PRISMA guidelines, a systematic review was conducted to assess the use of deep learning for caries diagnosis in bitewing radiographs. Literature searches were performed across Web of Science and PubMed databases for studies published before March 2025 that utilized deep learning for caries detection, segmentation, and classification using bitewing radiographs. Data extraction focused on model architectures, dataset characteristics, annotation processes, diagnostic performance metrics, and potential biases, as assessed by the QUADAS-2.

RESULTS

Twenty-three studies met the inclusion criteria, encompassing caries detection, segmentation, and severity classification. The most frequently applied deep learning models were classification models, such as ResNet and detection models, such as YOLO architectures. Dataset sizes varied widely, ranging from 112 to 8,539 images. Most studies reported high diagnostic performance, with accuracies ranging from 70% to 99%. Some AI models outperformed or matched the performance of human experts, particularly in detecting advanced carious lesions. However, considerable variability was observed in model architectures, dataset characteristics, the applied diagnostic performance metrics, and reporting standards. The risk of bias assessment revealed concerns in patient selection, index test interpretation, and reference standards, with all studies rated as having a high risk of bias in at least one domain.

CONCLUSION

The review identified challenges in currently developed deep learning models regarding methodological heterogeneity, lack of standardization, limited dataset diversity, insufficient clinical validation, and concerns about bias and data transparency. Nevertheless, all studies concluded that deep learning models are promising as an assistive diagnostic tool in caries diagnostics using bitewing radiography.