Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.

Journal: Japanese journal of radiology
Published Date: Jul 7, 2025

Abstract

PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection assist radiologists. As their performance using ultra-high-resolution CT (U-HRCT) images has not been evaluated, we investigated the usefulness of 0.25-mm slices at U-HRCT using the commercially available deep-learning-based lung nodule detection (DL-LND) system.

Authors

  • Haruka Higashibori
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minamiku, Hiroshima, 734-8551, Japan.
  • Wataru Fukumoto
    Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Sayaka Kusuda
    Department of Diagnostic Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minamiku, Hiroshima, 734-8551, Japan.
  • Kazushi Yokomachi
    Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Hidenori Mitani
    Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Yuko Nakamura
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Kazuo Awai
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.

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