Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

Journal: Japanese journal of radiology
Published Date: Sep 19, 2022

Abstract

PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data characteristics on the sensitivity and final diagnosis.

Authors

  • Naoki Toda
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
  • Masahiro Hashimoto
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan. m.hashimoto@rad.med.keio.ac.jp.
  • Yu Iwabuchi
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Misa Nagasaka
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Ryo Takeshita
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Minoru Yamada
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Yoshitake Yamada
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan. Electronic address: yamada@rad.med.keio.ac.jp.
  • Masahiro Jinzaki
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan.

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