Real-Time Artificial Intelligence Assistance in Endoscopic Detection of Early-Stage Gastric Carcinoma: A Multicenter Study
DOI:
https://doi.org/10.70088/71xe0390Keywords:
Artificial Intelligence, Endoscopy, Gastric Carcinoma, Real-Time Detection, Multicenter StudyAbstract
This study inquire the application of real-meter intelligence (AI) help in endoscopic detection of other-stage stomachal carcinoma across medical midpoint. On the integrating of AI algorithms into clinical workflows, the enquiry focuses to raise diagnostic truth and repress human mistake. The methodology after affect a multicenter study design, employing a combining of AI-labor image analysis and symptomatic techniques. Aboard shorten metre. Solvent certify substantial improvement in sensitiveness and specificity for early-stage stomachic carcinoma detection. The treatment increasingly highlights the significance for pattern, potential restriction, and future focussing for AI integration in endoscopic procedures.References
D. Popovic et al., "The importance of artificial intelligence in upper gastrointestinal endoscopy," Diagnostics, vol. 13, no. 18, p. 2862, 2023.
H. Luo et al., "Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study," The Lancet Oncology, vol. 20, no. 12, pp. 1645-1654, 2019.
N. Gonçalves, J. Chaves, I. Marques-Sá, M. Dinis-Ribeiro, and D. Libânio, "Early diagnosis of gastric cancer: Endoscopy and artificial intelligence," Best Practice & Research Clinical Gastroenterology, vol. 75, p. 101979, 2025.
H. Ali et al., "Artificial intelligence in gastrointestinal endoscopy: a comprehensive review," Annals of Gastroenterology, vol. 37, no. 2, p. 133, 2024.
M. Tokat, L. Van Tilburg, A. D. Koch, and M. C. Spaander, "Artificial intelligence in upper gastrointestinal endoscopy," Digestive Diseases, vol. 40, no. 4, pp. 395-408, 2022.
B. Zha, A. Cai, and G. Wang, "Diagnostic accuracy of artificial intelligence in endoscopy: umbrella review," JMIR Medical Informatics, vol. 12, p. e56361, 2024.
T. M. Berzin, S. Parasa, M. B. Wallace, S. A. Gross, A. Repici, and P. Sharma, "Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force," Gastrointestinal Endoscopy, vol. 92, no. 4, pp. 951-959, 2020.
J. Choi et al., "Convolutional neural network technology in endoscopic imaging: artificial intelligence for endoscopy," Clinical Endoscopy, vol. 53, no. 2, pp. 117-126, 2020.
M. Owais, M. Arsalan, J. Choi, T. Mahmood, and K. R. Park, "Artificial intelligence-based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis," Journal of Clinical Medicine, vol. 8, no. 7, p. 986, 2019.
P. Sinonquel, T. Eelbode, P. Bossuyt, F. Maes, and R. Bisschops, "Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy," Digestive Endoscopy, vol. 33, no. 2, pp. 242-253, 2021.
H. Messmann et al., "Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement," Endoscopy, vol. 54, no. 12, pp. 1211-1231, 2022.
Y. Mori et al., "Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective," Digestive Endoscopy, vol. 31, no. 4, pp. 378-388, 2019.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Xinran Wu (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
