Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.

Journal: Cancer cytopathology
Published Date: Dec 16, 2022

Abstract

BACKGROUND: Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The objective of this study was to demonstrate the accuracy of AI-based image analysis for thyroid fine-needle aspiration cytology (FNAC) and to propose its application in clinical practice.

Authors

  • Mitsuyoshi Hirokawa
    Department of Diagnostic Pathology and Cytology, Kuma Hospital, Kobe, Japan.
  • Hirohiko Niioka
    Graduate School of Engineering Science, Osaka University, 1-3 Machikane-Yama, Toyonaka, Osaka, 560-8531, Japan.
  • Ayana Suzuki
    Department of Diagnostic Pathology and Cytology, Kuma Hospital, Kobe, Japan.
  • Masatoshi Abe
    Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Yusuke Arai
    Department of Ophthalmology, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke-shi, Tochigi, Japan.
  • Hajime Nagahara
    Institute for Datability Science, Osaka University, Suita, Osaka, Japan.
  • Akira Miyauchi
    Department of Surgery, Kuma Hospital, Kobe, Japan.
  • Takashi Akamizu
    Department of Internal Medicine, Kuma Hospital, Kobe, Japan.

Keywords

External Resources

Popular Topics
Recent Journals