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Development of a computer-aided quality assurance support system for identifying hand X-ray image direction using deep convolutional neural network

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Abstract

The convenience of imaging has improved with digitization; however, there has been no progress in the methods used to prevent human error. Therefore, radiographic incidents and accidents are not prevented. In Japan, image interpretation is conducted for incident prevention; nevertheless, in some cases, incidents are overlooked. Thus, assistance from a computer-aided quality assurance support system is important. This study developed a method to identify hand image direction, which is an elementary technology of a computer-aided quality assurance support system. In total, 14,236 hand X-ray images were used to classify hand directions (upward, downward, rightward, and leftward) commonly evaluated in clinical settings. The accuracy of the conventional classification method using original images, classification method with histogram equation images, and a novel classification method using binarization images for background removal via U-Net segmentation was evaluated. The following classification accuracy rates were achieved: 89.20% if the original image was input, 99.10% if the histogram equation image was input, and 99.70% if binarization images for background removal via U-Net segmentation was input. Our computer-aided quality assurance support system can be used to identify hand direction with high accuracy.

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Acknowledgements

The authors would like to thank Takuto Ishibashi for providing assistance.

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Authors and Affiliations

  1. Department of Radiological Technology, School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, Niigata, 951-8518, Japan

    Mitsuru Sato, Yohan Kondo & Masashi Okamoto

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  1. Mitsuru Sato
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  2. Yohan Kondo
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  3. Masashi Okamoto
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Correspondence to Mitsuru Sato.

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This work has not been published before, in part or in its entirety. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Sato, M., Kondo, Y. & Okamoto, M. Development of a computer-aided quality assurance support system for identifying hand X-ray image direction using deep convolutional neural network. Radiol Phys Technol 15, 358–366 (2022). https://doi.org/10.1007/s12194-022-00675-1

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  • DOI: https://doi.org/10.1007/s12194-022-00675-1

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