Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network-Based Image Classification System.

Journal: The Laryngoscope
PMID: 34000069

Abstract

OBJECTIVES/HYPOTHESIS: There may be an interobserver variation in the diagnosis of laryngeal disease based on laryngoscopic images according to clinical experience. Therefore, this study is aimed to perform computer-assisted diagnosis for common laryngeal diseases using deep learning-based disease classification models.

Authors

  • Won Ki Cho
    Departments of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
  • Yeong Ju Lee
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Hye Ah Joo
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • In Seong Jeong
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Yeonjoo Choi
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Soon Yuhl Nam
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Sang Yoon Kim
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Seung-Ho Choi
    Departments of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. Electronic address: shchoi@amc.seoul.kr.

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