Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location.

Journal: European journal of radiology
Published Date: Jul 22, 2023

Abstract

PURPOSE: Computer-aided diagnosis (CAD), which assists in the interpretation of chest radiographs, is becoming common. However, few studies have evaluated the benefits and pitfalls of CAD in the real world. This study aimed to evaluate the independent performance of commercially available deep learning-based automatic detection (DLAD) software, EIRL Chest X-ray Lung Nodule, in a cohort that included patients with background pulmonary abnormalities often encountered in clinical situations.

Authors

  • Midori Ueno
    Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishiku, Kitakyushu, Fukuoka, 807-8555, Japan.
  • Kotaro Yoshida
    1 Department of Radiology, Radiology Informatics Laboratory, Mayo Clinic, 3507 17th Ave NW, Rochester, MN 55901.
  • Atsushi Takamatsu
    Department of Radiology, Kanazawa University Graduate School of Medical Science, 1-13 Takaramachi, Kanazawa City, Ishikawa Prefecture 920-8641, Japan. Electronic address: tomat0401@gmail.com.
  • Takeshi Kobayashi
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan.
  • Takatoshi Aoki
    Department of Radiology, University of Occupational and Environmental Health School of Medicine, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan.
  • Toshifumi Gabata
    Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan.

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