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

Artificial Intelligence (1360) Innovations and Technology in Cancer Care (422) Clinical Informatics (1014) Clinical Cancer Research (83) Imaging Informatics (193) Gastroenterology and Hepatology (67) Cancer Prognosis Models and Machine Learning (13) Methods in Cancer Research (28)

Published on in Vol 24, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27694, first published .
The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study

The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study

The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study

Authors of this article:

Pei-Chin Chen1, 2 Author Orcid Image ;   Yun-Ru Lu2, 3 Author Orcid Image ;   Yi-No Kang4, 5, 6, 7 Author Orcid Image ;   Chun-Chao Chang8 Author Orcid Image
Article Authors Cited by (16) Tweetations (6) Metrics

Journals

  1. Panarese A, Saito Y, Zagari R. Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need?. Artificial Intelligence in Gastrointestinal Endoscopy 2023;4(1):1 View
  2. Luo Q, Yang H, Hu B. Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection. Journal of Digestive Diseases 2022;23(12):666 View
  3. HALVORSEN N, MORI Y. Artificial intelligence and upper gastrointestinal endoscopy: what is the optimal study design?. Minerva Surgery 2023;78(1) View
  4. Sy-Janairo M, Janairo J. Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review. Journal of Gastrointestinal Cancer 2024;55(1):47 View
  5. Mirza O, Alsobhi A, Hasanin T, Ishak M, Karim F, Mostafa S. Computer Aided Diagnosis for Gastrointestinal Cancer Classification Using Hybrid Rice Optimization With Deep Learning. IEEE Access 2023;11:76321 View
  6. Horiuchi Y, Hirasawa T, Fujisaki J. Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. Clinical Endoscopy 2024;57(1):11 View
  7. K. P, Kanna R. K, Li G, Subramaniam U, Sekar M. Diagnosis of gastric cancer in role of endoscopic imaging techniques in artificial intelligence and machine learning applications: An overview. E3S Web of Conferences 2024;491:03016 View
  8. Almasoud A, Maray M, Alkahtani H, Alotaibi F, Alnfiai M, Sayed A. Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning. IEEE Access 2024;12:23122 View
  9. Pisani A, Libânio D, Kunovský L. Shedding a new light on gastric cancer screening. United European Gastroenterology Journal 2024;12(6):662 View
  10. Fernández Aceñero M, Díaz del Arco C. Hereditary Gastrointestinal Tumor Syndromes: When Risk Comes with Your Genes. Current Issues in Molecular Biology 2024;46(7):6440 View
  11. Zha B, Cai A, Wang G. Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review. JMIR Medical Informatics 2024;12:e56361 View
  12. Almarshad F, Balaji P, Syed L, Aljohani E, Dharmarajlu S, Vaiyapuri T, AlAseem N. An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification. IEEE Access 2024;12:137237 View

Books/Policy Documents

  1. Saghazadeh A, Rezaei N. Handbook of Cancer and Immunology. View
  2. Sahu V, Ranjan A, Paul M, Nagar S, Devarajan S, Aich J, Basu S. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications. View
  3. Lu M, Chai Y. Proceedings of 2023 Chinese Intelligent Systems Conference. View