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Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT

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Abstract

Objectives

The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the relationship between them on CBCT.

Materials and methods

A dataset of 254 CBCT scans with annotations by radiologists was used for the training, the validation, and the test. The proposed approach consisted of two modules: (1) detection and pixel-wise segmentation of M3 and MC based on U-Nets; (2) M3-MC relation classification based on ResNet-34. The performances were evaluated with the test set. The classification performance of our approach was compared with two residents in oral and maxillofacial radiology.

Results

For segmentation performance, the M3 had a mean Dice similarity coefficient (mDSC) of 0.9730 and a mean intersection over union (mIoU) of 0.9606; the MC had a mDSC of 0.9248 and a mIoU of 0.9003. The classification models achieved a mean sensitivity of 90.2%, a mean specificity of 95.0%, and a mean accuracy of 93.3%, which was on par with the residents.

Conclusions

Our approach based on CNNs demonstrated an encouraging performance for the automatic detection and evaluation of the M3 and MC on CBCT.

Clinical relevance

An automated approach based on CNNs for detection and evaluation of M3 and MC on CBCT has been established, which can be utilized to improve diagnostic efficiency and facilitate the precision diagnosis and treatment of M3.

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Funding

This study was supported by the Program for New Clinical Techniques and Therapies of Peking University School and Hospital of Stomatology (no. PKUSSNCT-19B08).

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Authors and Affiliations

  1. Center for TMD and Orofacial Pain, Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, No. 22 Zhong Guan Cun South Ave, Beijing, 100081, People’s Republic of China

    Mu-Qing Liu, Wei-Yu Mao, Yuan Li, Xiao-Han Zhang & Kai-Yuan Fu

  2. National Clinical Research Center for Oral Diseases, Beijing, China

    Mu-Qing Liu, Wei-Yu Mao, Yuan Li, Xiao-Han Zhang & Kai-Yuan Fu

  3. National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China

    Mu-Qing Liu, Wei-Yu Mao, Yuan Li, Xiao-Han Zhang & Kai-Yuan Fu

  4. Beijing Key Laboratory of Digital Stomatology, Beijing, China

    Mu-Qing Liu, Wei-Yu Mao, Yuan Li, Xiao-Han Zhang & Kai-Yuan Fu

  5. Deepcare, Inc, Beijing, China

    Zi-Neng Xu, Hai-Long Bai & Peng Ding

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  1. Mu-Qing Liu
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  2. Zi-Neng Xu
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Correspondence to Kai-Yuan Fu.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of Peking University School and Hospital of Stomatology and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Institutional Review Board of Peking University School and Hospital of Stomatology (PKUSSIRB-201949131).

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Written informed consent was not required for this study because all the included patients were collected retrospectively. Exemption of informed consent will not affect the rights and health of included patients. The application for free informed consent has been approved by the Institutional Review Board.

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The authors declare no competing interests.

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Liu, MQ., Xu, ZN., Mao, WY. et al. Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT. Clin Oral Invest 26, 981–991 (2022). https://doi.org/10.1007/s00784-021-04082-5

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