Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

Abstract

Authors

• Tomohiro Wataya
  Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan. wataya-osk@umin.ac.jp.
• Masahiro Yanagawa
  Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
• Mitsuko Tsubamoto
  Department of Future Diagnostic Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
• Tomoharu Sato
  Radiation Oncology Department, The Cancer Institute Hospital, Japanese Foundation for Cancer Research.
• Daiki Nishigaki
  Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
• Kosuke Kita
  Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
• Kazuki Yamagata
  Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
• Yuki Suzuki
  Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
• Akinori Hata
  Department of Radiology, Osaka University Graduate School of Medicine.
• Shoji Kido
  Graduate School of Medicine, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan.
• Noriyuki Tomiyama
  Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.

Keywords

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