Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison.

Journal: Gastrointestinal endoscopy
Published Date: Sep 4, 2021

Abstract

BACKGROUND AND AIMS: Endoscopic differential diagnoses of gastric mucosal lesions (benign gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging. We aimed to develop and validate convolutional neural network-based artificial intelligence (AI) models: lesion detection, differential diagnosis (AI-DDx), and invasion depth (AI-ID; pT1a vs pT1b among EGC) models.

Authors

  • Joon Yeul Nam
    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
  • Hyung Jin Chung
    Bio Imaging and Signal Processing Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute for Science and Technology, Daejeon, Korea.
  • Kyu Sung Choi
    Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Hyuk Lee
    Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Tae Jun Kim
    Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Hosim Soh
    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
  • Eun Ae Kang
    Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
  • Soo-Jeong Cho
    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
  • Jong Chul Ye
  • Jong Pil Im
    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
  • Sang Gyun Kim
    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Joo Sung Kim
    Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea; Department of Internal Medicine, Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea. Electronic address: jooskim@snu.ac.kr.
  • Hyunsoo Chung
    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. hschungmd@gmail.com.
  • Jeong-Hoon Lee

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