Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

Journal: BMC medical informatics and decision making
PMID: 38811961

Abstract

BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses.

Authors

  • Junhu Tai
    Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea.
  • Munsoo Han
    Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea.
  • Bo Yoon Choi
    Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea.
  • Sung Hoon Kang
    Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Hyeongeun Kim
    Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea.
  • Jiwon Kwak
    Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea.
  • Dabin Lee
    Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, Seoul, Republic of Korea.
  • Tae Hoon Lee
    Division of Colon and Rectal Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
  • Yongwon Cho
    Department of Convergence Medicine, Asan Medical Center, College of Medicine, University of Ulsan, 88, Olympic-ro 43-gil, Seoul, 05505, South Korea.
  • Tae Hoon Kim
    Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonjuro, Gangnam-Gu, Seoul, 06273, Republic of Korea.

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