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Objectives <p>Dental caries remains a significant global health concern. Recognising the diagnostic potential of cone-beam computed tomography (CBCT) in caries assessment, this study aimed to develop an artificial intelligence (AI)-driven tool for accurate caries localisation and classification on CBCT images, thereby enhancing early diagnosis and precise treatment planning.</p> Materials and methods <p>A three-dimensional (3D) convolutional neural network (CNN) was developed using a large annotated dataset comprising 1,778 single-tooth CBCT images. The network’s performance in localising and classifying multi-stage caries was compared with that of three dentists. Performance metrics included precision, recall, F1-score, Dice similarity coefficient (DSC), and the area under the receiver operating characteristic (ROC) curve (AUC).</p> Results <p>The proposed CNN achieved overall precision, recall, and DSC values of 0.712, 0.899, and 0.776, respectively, for lesion localisation. In comparison, the average metrics values for the dentists were 0.622, 0.886, and 0.700. For caries classification, the CNN achieved precision, recall, and F1-score values of 0.855, 0.857, and 0.856, respectively, whereas the corresponding values for the dentists were 0.700, 0.684, and 0.678. Overall, the CNN significantly outperformed the dentists in both localisation and classification tasks.</p> Conclusions <p>This study developed a high-performance 3D CNN for the localisation and classification of multi-stage caries on CBCT images. The CNN demonstrated significantly superior diagnostic performance compared to a group of three dentists, underscoring its potential for clinical integration.</p> Clinical relevance <p>The integration of AI into CBCT image analysis may improve the efficiency and accuracy of caries diagnosis. The proposed CNN represents a promising tool to enhance early diagnosis and precise treatment planning, potentially supporting clinical decision-making in dental practice.</p>

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Localisation and classification of multi-stage caries on CBCT images with a 3D convolutional neural network

  • 期刊论文

  • 发表日期：

    2025年4月14日

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  • Shuai Qi,
  • Yujie Fu,
  • Haoxuan Shan,
  • Genqiang Ren,
  • Yufei Chen,
  • Qi Zhang

摘要

Objectives

Dental caries remains a significant global health concern. Recognising the diagnostic potential of cone-beam computed tomography (CBCT) in caries assessment, this study aimed to develop an artificial intelligence (AI)-driven tool for accurate caries localisation and classification on CBCT images, thereby enhancing early diagnosis and precise treatment planning.

Materials and methods

A three-dimensional (3D) convolutional neural network (CNN) was developed using a large annotated dataset comprising 1,778 single-tooth CBCT images. The network’s performance in localising and classifying multi-stage caries was compared with that of three dentists. Performance metrics included precision, recall, F1-score, Dice similarity coefficient (DSC), and the area under the receiver operating characteristic (ROC) curve (AUC).

Results

The proposed CNN achieved overall precision, recall, and DSC values of 0.712, 0.899, and 0.776, respectively, for lesion localisation. In comparison, the average metrics values for the dentists were 0.622, 0.886, and 0.700. For caries classification, the CNN achieved precision, recall, and F1-score values of 0.855, 0.857, and 0.856, respectively, whereas the corresponding values for the dentists were 0.700, 0.684, and 0.678. Overall, the CNN significantly outperformed the dentists in both localisation and classification tasks.

Conclusions

This study developed a high-performance 3D CNN for the localisation and classification of multi-stage caries on CBCT images. The CNN demonstrated significantly superior diagnostic performance compared to a group of three dentists, underscoring its potential for clinical integration.

Clinical relevance

The integration of AI into CBCT image analysis may improve the efficiency and accuracy of caries diagnosis. The proposed CNN represents a promising tool to enhance early diagnosis and precise treatment planning, potentially supporting clinical decision-making in dental practice.