Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: differences in detection ability among various image reconstruction methods.

Journal: Japanese journal of radiology
Published Date: May 9, 2025

Abstract

PURPOSE: This study aimed to investigate the performance of an artificial intelligence (AI)-based lung nodule detection program in ultra-low-dose CT (ULDCT) imaging, with a focus on the influence of various image reconstruction methods on detection accuracy.

Authors

  • Nanae Tsuchiya
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan. ntsuchi@med-ryukyu.ac.jp.
  • Shifumi Kobayashi
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.
  • Ryo Nakachi
    Division of Radiology, Department of Medical Technology, University of the Ryukyus Hospital, Okinawa, Japan.
  • Yukari Tomori
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.
  • Akira Yogi
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.
  • Gyo Iida
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.
  • Junji Ito
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.
  • Akihiro Nishie
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 1076 Kiyuna, Ginowan-shi, Okinawa, 901-2720, Japan.

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