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

Artificial Intelligence (1371) Machine Learning (1465) Decision Support for Health Professionals (1185) Imaging Informatics (193) Innovations and Technology in Cancer Care (423)

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

Journals

  1. Gong E, Bang C, Lee J, Seo S, Yang Y, Baik G, Kim J. No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification. Journal of Personalized Medicine 2022;12(6):963 View
  2. Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Review of Gastroenterology & Hepatology 2022;16(1):21 View
  3. Hamamoto R, Koyama T, Kouno N, Yasuda T, Yui S, Sudo K, Hirata M, Sunami K, Kubo T, Takasawa K, Takahashi S, Machino H, Kobayashi K, Asada K, Komatsu M, Kaneko S, Yatabe Y, Yamamoto N. Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information. Experimental Hematology & Oncology 2022;11(1) View
  4. El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artificial Intelligence in Gastroenterology 2021;2(5):124 View
  5. Bang C. Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders. The Korean Journal of Helicobacter and Upper Gastrointestinal Research 2021;21(4):300 View
  6. Gong E, Bang C, Lee J, Yang Y, Baik G. Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images. Journal of Personalized Medicine 2022;12(9):1361 View
  7. Kim H, Gong E, Bang C, Lee J, Suk K, Baik G. Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis. Journal of Personalized Medicine 2022;12(4):644 View
  8. Bang C, Lee J, Baik G. Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis. Journal of Medical Internet Research 2021;23(12):e33267 View
  9. Xie F, Zhang K, Li F, Ma G, Ni Y, Zhang W, Wang J, Li Y. Diagnostic accuracy of convolutional neural network–based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointestinal Endoscopy 2022;95(4):599 View
  10. Gong E, Bang C, Jung K, Kim S, Kim J, Seo S, Lee U, Maeng Y, Lee Y, Lee J, Baik G, Lee J. Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study. Journal of Personalized Medicine 2022;12(7):1052 View
  11. Chung J, Oh D, Park J, Kim S, Lim Y. Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model. Diagnostics 2023;13(8):1389 View
  12. Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Seminars in Cancer Biology 2023;93:83 View
  13. Gong E, Bang C, Lee J, Baik G, Lim H, Jeong J, Choi S, Cho J, Kim D, Lee K, Shin S, Sigmund D, Moon B, Park S, Lee S, Bang K, Son D. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study. Endoscopy 2023;55(08):701 View
  14. Gong E, Bang C, Lee J, Jeong H, Baik G, Jeong J, Dick S, Lee G. Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study. Journal of Medical Internet Research 2023;25:e50448 View
  15. Gong E, Bang C. Interpretation of Medical Images Using Artificial Intelligence: Current Status and Future Perspectives. The Korean Journal of Gastroenterology 2023;82(1):43 View
  16. Vania M, Tama B, Maulahela H, Lim S. Recent Advances in Applying Machine Learning and Deep Learning to Detect Upper Gastrointestinal Tract Lesions. IEEE Access 2023;11:66544 View
  17. Klang E, Soroush A, Nadkarni G, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics 2023;13(24):3613 View
  18. Wu R, Qin K, Fang Y, Xu Y, Zhang H, Li W, Luo X, Han Z, Liu S, Li Q. Application of the convolution neural network in determining the depth of invasion of gastrointestinal cancer: a systematic review and meta-analysis. Journal of Gastrointestinal Surgery 2024;28(4):538 View
  19. Gong E, Bang C, Lee J. Computer‐aided diagnosis in real‐time endoscopy for all stages of gastric carcinogenesis: Development and validation study. United European Gastroenterology Journal 2024;12(4):487 View
  20. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3):187 View
  21. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3 - Correct DOI):187 View

Books/Policy Documents

  1. Singh R, Masih G, Joshi R, Sharma S, Singh A, Medhi B. Biomarkers in Cancer Detection and Monitoring of Therapeutics. View