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Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

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Abstract

Objectives

The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.

Methods

Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.

Results

Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.

Conclusions

The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.

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Acknowledgements

We thank Karl Embleton, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.

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Author notes

  1. Motoki Fukuda and Kyoko Inamoto contributed equally to this work

Authors and Affiliations

  1. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan

    Motoki Fukuda, Yoshiko Ariji & Eiichiro Ariji

  2. Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan

    Kyoko Inamoto, Naoki Shibata & Kazuhiko Nakata

  3. Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan

    Yudai Yanashita, Shota Kutsuna & Hiroshi Fujita

  4. Department of Oral Radiology, Asahi University, Mizuho, Japan

    Akitoshi Katsumata

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  1. Motoki Fukuda
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Corresponding author

Correspondence to Motoki Fukuda.

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Conflict of interest

None of the authors have any conflict of interest associated with this study.

Ethical approval

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions.

Informed consent

Informed consent was obtained from all patients for being included in the study. This study obtained ethical approval from Aichi-Gakuin University ethics committee (No. 496).

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Fukuda, M., Inamoto, K., Shibata, N. et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol 36, 337–343 (2020). https://doi.org/10.1007/s11282-019-00409-x

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  • DOI: https://doi.org/10.1007/s11282-019-00409-x

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