This paper is in the following e-collection/theme issue:

Artificial Intelligence (1216) Machine Learning (1401) Decision Support for Health Professionals (1147) Imaging Informatics (187) Innovations and Technology in Cancer Care (405)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25167, first published .
Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study

Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study

Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study

Authors of this article:

Chang Seok Bang1, 2, 3, 4 Author Orcid Image ;   Hyun Lim1 Author Orcid Image ;   Hae Min Jeong1 Author Orcid Image ;   Sung Hyeon Hwang1 Author Orcid Image
Article Authors Cited by (22) Tweetations (4) Metrics

Chang Seok Bang   1, 2, 3, 4 , MD, PhD ;   Hyun Lim   1 , MD, PhD ;   Hae Min Jeong   1 , MD ;   Sung Hyeon Hwang   1 , MD

1 Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea

2 Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Republic of Korea

3 Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea

4 Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea

Corresponding Author:

  • Chang Seok Bang, MD, PhD
  • Department of Internal Medicine
  • Hallym University College of Medicine
  • Sakju-ro 77
  • Chuncheon, 24253
  • Republic of Korea
  • Phone: 82 332405821
  • Fax: 82 332418064
  • Email: csbang@hallym.ac.kr