Objectives: Periapical periodontitis and caries are common chronic oral diseases affecting most teenagers and adults worldwide. The purpose of this study was to develop an evaluation tool to automatically detect dental caries and periapical periodontitis on periapical radiographs using deep learning.
Methods: A modified deep learning model was developed using a large dataset (4129 images) with high-quality annotations to support the automatic detection of both dental caries and periapical periodontitis. The performance of the model was compared to the classification performance of dentists.
Results: The deep learning model automatically distinguished dental caries with an F1-score of 0.829 and periapical periodontitis with an F1-score of 0.828. The comparison of model-only and expert-only detection performance showed that the accuracy of the fully automatic method was significantly higher than that of the young dentists. With deep learning assistance, the experts not only reached a higher diagnostic accuracy with an average F1-score of 0.7844 for dental caries and 0.8208 for periapical periodontitis compared to expert-only scenarios, but also increased inter-observer agreement from 0.585/0.590 to 0.726/0.713 for dental caries and from 0.623/0.563 to 0.752/0.740 for periapical periodontitis.
Conclusions: Based on these experimental results, deep learning can improve the accuracy and consistency of evaluating dental caries and periapical periodontitis on periapical radiographs.
Clinical significance: Deep learning models can improve accuracy and consistency and reduce the workload of dentists, making artificial intelligence a powerful tool for clinical practice.
Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Diagnostic imaging; Endodontics; Machine learning.
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