Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield.

Journal: Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Published Date: Sep 13, 2023

Abstract

OBJECTIVES: Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model.

Authors

  • Kyung Seok Choi
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • DoGyeom Park
    Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea.
  • Jin Su Kim
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Dae Young Cheung
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Bo-In Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Young-Seok Cho
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Jin Il Kim
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Seungchul Lee
    Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 223, 5th Engineering Building 77 Cheongam-ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea. Electronic address: seunglee@postech.ac.kr.
  • Han Hee Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.

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