Evidence map›Paper›PMID 40227550›Full record

ArticleClinical oral investigations2025

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

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

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Article in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shuai Qi *Department of Endodontics, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Stomatological Hospital and Dental School of Tongji University, Shanghai, 200072, China.
Yujie Fu *Department of Endodontics, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Stomatological Hospital and Dental School of Tongji University, Shanghai, 200072, China.
Haoxuan ShanCollege of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
Genqiang RenCollege of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
Yufei ChenCollege of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China. yufeichen@tongji.edu.cn.
Qi ZhangDepartment of Endodontics, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Stomatological Hospital and Dental School of Tongji University, Shanghai, 200072, China. qizhang@tongji.edu.cn.

Funding

National Natural Science Foundation of China 82170945Shanghai Municipal Hospital Development Center SHDC2020CR3058B
6 · The paper itself

Abstract

objectivesDental 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

methodsA 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).

resultsThe 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.

conclusionsThis 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.

Indexed as

Cone-Beam Computed TomographyDental CariesImaging, Three-DimensionalNeural Networks, ComputerConvolutional Neural NetworksHumansArtificial intelligenceCone-beam computed tomographyConvolutional neural networksDeep learningDental cariesDiagnostic imaging

Identifiers

PMID40227550

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.