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Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

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Oral Radiology Aims and scope Submit manuscript

Abstract

Objectives

To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance.

Methods

Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents.

Results

The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents.

Conclusions

The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.

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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

    Makoto Murata, Yoshiko Ariji, Yasufumi Ohashi, Motoki Fukuda, Takuma Funakoshi, Yoshitaka Kise, Michihito Nozawa & Eiichiro Ariji

  2. Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan

    Taisuke Kawai

  3. Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan

    Akitoshi Katsumata

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

    Hiroshi Fujita

Authors
  1. Makoto Murata
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  2. Yoshiko Ariji
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  3. Yasufumi Ohashi
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  4. Taisuke Kawai
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  5. Motoki Fukuda
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  6. Takuma Funakoshi
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  7. Yoshitaka Kise
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  8. Michihito Nozawa
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  9. Akitoshi Katsumata
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  10. Hiroshi Fujita
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  11. Eiichiro Ariji
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Corresponding author

Correspondence to Yoshiko Ariji.

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Conflict of interest

Makoto Murata, Yoshiko Ariji, Yasufumi Ohashi, Taisuke Kawai, Motoki Fukuda, Takuma Funakoshi, Yoshitaka Kise, Michihito Nozawa, Akitoshi Katsumata, Hiroshi Fujita, and Eiichiro Ariji declare that they have no conflict of interest.

Human rights statement

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.

Animal rights statement

This article does not contain any studies with animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all patients for being included in the study.

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Cite this article

Murata, M., Ariji, Y., Ohashi, Y. et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 35, 301–307 (2019). https://doi.org/10.1007/s11282-018-0363-7

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  • DOI: https://doi.org/10.1007/s11282-018-0363-7

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