Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection.

Journal: Neuroradiology
Published Date: Oct 6, 2020

Abstract

PURPOSE: To elucidate the effect of deep learning-based computer-assisted detection (CAD) on the performance of different-level physicians in detecting intracranial haemorrhage using CT.

Authors

  • Yoshiyuki Watanabe
    Department of Future Diagnostic Radiology, Osaka University Graduate School of Medicine.
  • Takahiro Tanaka
    Dai Nippon Printing Co., Ltd., Tokyo, Japan.
  • Atsushi Nishida
    Dai Nippon Printing Co., Ltd., Tokyo, Japan.
  • Hiroto Takahashi
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Masahiro Fujiwara
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Takuya Fujiwara
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Atsuko Arisawa
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Hiroki Yano
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Noriyuki Tomiyama
    Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
  • Hajime Nakamura
    Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Kenichi Todo
    Department of Neurology, Tokyo Women's Medical University Hospital.
  • Kazuhisa Yoshiya
    Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Osaka, Japan.

Keywords

External Resources

Popular Topics
Recent Journals