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Detection and classification of mandibular fracture on CT scan using deep convolutional neural network

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Abstract

Objectives

This study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT).

Materials and methods

Between January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by DICE, accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

Results

One thousand five hundred six mandibular fractures in nine subregions of 686 patients were diagnosed. The DICE of mandible segmentation using U-Net was 0.943. The accuracies of nine subregions were all above 90%, with a mean AUC of 0.956.

Conclusions

CNNs showed comparable reliability and accuracy in detecting and classifying mandibular fractures on CT.

Clinical relevance

The algorithm for automatic detection and classification of mandibular fractures will help improve diagnostic efficiency and provide expertise to areas with lower medical levels.

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Acknowledgements

The authors acknowledge the assistance of the radiological department of Peking University School and Hospital of Stomatology in Beijing, China, for database establishment.

The first author Dr. Xuebing Wang acknowledges the care and assistance from Dr. Ruiliu Li, Dr. Lihang Shen, Dr. Hang Wang, Dr. Chengyi Wang, Dr. Huiyu Peng, Dr. Xiyue Wang, and Feier Wang.

Funding

This study was supported by the National Key Research and Development Program of China (2019YFF0302401). The funding agencies had no role in study design, implementation procedures, analysis of results, or preparation of the manuscript.

Author information

Author notes

  1. Xuebing Wang and Zineng Xu contributed equally to the work and share first authorship.

Authors and Affiliations

  1. Department of Oral and Maxillofacial SurgeryNational Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital StomatologyNational Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, No 22 Zhongguancun South Road, Beijing, 100081, People’s Republic of China

    Xuebing Wang, Yanhang Tong, Bimeng Jie, Yi Zhang & Yang He

  2. Deepcare, Inc, Beijing, China

    Zineng Xu, Peng Ding & Hailong Bai

  3. Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    Long Xia

Authors
  1. Xuebing Wang
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  4. Long Xia
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  9. Yang He
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Contributions

Yang He conceived the ideas. Xuebing Wang participated in the study design and collected and annotated all the data. Zineng Xu developed the algorithm. Xuebing Wang and Zineng Xu contributed equally to the article writing. Yanhang Tong and Long Xia participated in data annotation. Bimeng Jie contributed to the polish of the article. Hailong Bai and Peng Ding provided technical support. Yi Zhang provided the research platform. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yang He.

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Ethics approval

All the procedure was approved by the Ethics Committee of Peking University School and Hospital of Stomatology (protocol No. PKUSSIRB-202054056) and was conducted in accordance with the relevant guidelines and regulations.

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For this type of study, formal consent is not required.

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The authors declare no competing interests.

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Cite this article

Wang, X., Xu, Z., Tong, Y. et al. Detection and classification of mandibular fracture on CT scan using deep convolutional neural network. Clin Oral Invest 26, 4593–4601 (2022). https://doi.org/10.1007/s00784-022-04427-8

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  • DOI: https://doi.org/10.1007/s00784-022-04427-8

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