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An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network

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Abstract

Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation mapping (Grad-CAM) technique was used to explore the area of maximum impact on the model decisions in the tooth slices. The six CNN models performed remarkably well in OIERR grading, with a mean accuracy of 0.92, surpassing that of the orthodontists (mean accuracy of 0.82). EfficientNet-B4 trained with fivefold cross-validation emerged as the final OIERR grading system, with a high accuracy of 0.94. Grad-CAM revealed that the apical region had the greatest effect on the OIERR grading system. The six CNNs demonstrated excellent OIERR grading and outperformed orthodontists. The proposed OIERR grading system holds potential as a reliable diagnostic support for orthodontists in clinical practice.

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Data Availability

The research datasets in this study are not publicly available to protect patient privacy, but are available from the corresponding authors upon reasonable request.

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Funding

This study was funded in part by Chongqing Municipal Health Commission Program (2020GDRC002), Program for Youth Innovation in Future Medicine of Chongqing Medical University (W0055), Chongqing Young and Middle-aged Medical High-end Talent Studio, Young Clinical Research Fund of the Chinese Stomatological Association (CSA-O2022-07), Scientific and Technological Innovation Project of Construction of Double City Economic Circle in Chengdu-Chongqing Area of Chongqing Education Commission (KJCX2020017), and Open project of the Shanxi Provincial Key Laboratory for Prevention and Treatment of Oral Diseases and New Materials (KF2020-01).

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Authors and Affiliations

  1. College of Stomatology, Chongqing Medical University, Chongqing, 401147, China

    Shuxi Xu, Houli Peng, Lanxin Yang, Wenjie Zhong, Xiang Gao & Jinlin Song

  2. Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China

    Shuxi Xu, Houli Peng, Lanxin Yang, Wenjie Zhong, Xiang Gao & Jinlin Song

  3. Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China

    Shuxi Xu, Houli Peng, Lanxin Yang, Wenjie Zhong, Xiang Gao & Jinlin Song

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  1. Shuxi Xu
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  2. Houli Peng
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  4. Wenjie Zhong
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  5. Xiang Gao
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Contributions

Study design, SXX, HLP, LXY, WJZ, XG, and JLS; data acquisition, SXX; data analysis, SXX, HLP, and LXY; clinical studies, SXX, HLP, and LXY; manuscript writing, SXX, HLP, and LXY; manuscript editing, SXX, HLP, LXY, WJZ, XG, and JLS. All authors have given approval to the final version of the manuscript.

Corresponding authors

Correspondence to Xiang Gao or Jinlin Song.

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Ethics Approval

This study was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Stomatological Hospital of Chongqing Medical University (CQHS-REC-2023 (LSNo. 039)). The informed consent was waived in view of the retrospective nature and low risk involved of this study.

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The authors declare no competing interests.

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Xu, S., Peng, H., Yang, L. et al. An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01045-6

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  • DOI: https://doi.org/10.1007/s10278-024-01045-6

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