[Evaluation of Radiograph Accuracy in Skull X-ray Images Using Deep Learning].

Journal: Nihon Hoshasen Gijutsu Gakkai zasshi
PMID: 35046219

Abstract

PURPOSE: Accurate positioning is essential for radiography, and it is especially important to maintain image reproducibility in follow-up observations. The decision on re-taking radiographs is entrusting to the individual radiological technologist. The evaluation is a visual and qualitative evaluation and there are individual variations in the acceptance criteria. In this study, we propose a method of image evaluation using a deep convolutional neural network (DCNN) for skull X-ray images.

Authors

  • Hideyoshi Mitsutake
    Department of Radiological Technology, Teikyo University Hospital.
  • Haruyuki Watanabe
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Aya Sakaguchi
    School of Radiological Technology, Gunma Prefectural College of Health Sciences (Current address: Department of Radiological Technology, Seikei-kai Chiba Medical Center).
  • Kiyoshi Uchiyama
    Department of Radiological Technology, Teikyo University Hospital.
  • Yongbum Lee
    School of Health Sciences, Faculty of Medicine, Niigata University.
  • Norio Hayashi
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Masayuki Shimosegawa
    School of Radiological Technology, Gunma Prefectural College of Health Sciences.
  • Toshihiro Ogura
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.

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