External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.

Journal: Japanese journal of radiology
PMID: 39613978

Abstract

PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the commercially available deep-learning-based lung nodule detection (DL-LND) using LDCT images at Japanese lung cancer screening (LCS).

Authors

  • Wataru Fukumoto
    Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Yuki Yamashita
    School of Medicine, Hiroshima University, 1-2-3 Kasumi, Minamiku, Hiroshima, 734-8551, Japan.
  • Ikuo Kawashita
    Department of Clinical Radiology, Hiroshima International University, 555-36, kurosegakuendai, Higashihiroshima, Hiroshima, 739-2695, Japan.
  • Toru Higaki
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.
  • Asako Sakahara
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minamiku, 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.
  • Yoshikazu Awaya
    Department of Respiratory Medicine, Miyoshi Central Hospital, 10531 Higashi-Sakaya-cho, Miyoshi, Hiroshima, 728-8502, Japan.
  • Kazuo Awai
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.

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