Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation.

Journal: Japanese journal of radiology
PMID: 36173510

Abstract

PURPOSE: We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID).

Authors

  • Makiko Ishihara
    Department of Diagnostic Imaging Center, Toranomon Hospital, 1-8-1 Akasaka Intercity AIR 5F, Akasaka, Minato-ku, Tokyo, 107-0052, Japan. m-ishihara@toranomon.gr.jp.
  • Masato Shiiba
    Department of Diagnostic Imaging Center, Toranomon Hospital, 1-8-1 Akasaka Intercity AIR 5F, Akasaka, Minato-ku, Tokyo, 107-0052, Japan.
  • Hirotaka Maruno
    Department of Radiology, Toranomon Hospital, 2-2-2 toranomon, Minato-ku, Tokyo, 105-8470, Japan.
  • Masayuki Kato
    Department of Health Management Center, Toranomon Hospital, 1-8-1 Akasaka Intercity AIR 5F, Akasaka, Minato-ku, Tokyo, 107-0052, Japan.
  • Yuki Ohmoto-Sekine
    Department of Health Management Center, Toranomon Hospital, 1-8-1 Akasaka Intercity AIR 5F, Akasaka, Minato-ku, Tokyo, 107-0052, Japan.
  • Choppin Antoine
    LPIXEL Inc, 1-6-1 Ohtemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
  • Yasuyoshi Ouchi
    Department of Geriatric Medicine, Toranomon Hospital, 2-2-2 toranomon, Minato-ku, Tokyo, 105-8470, Japan.

Keywords

External Resources

Popular Topics
Recent Journals