Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	67549
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Application of a convolutional neural network in detecting and classifying gastric cancer
Manuscript Source	Invited Manuscript
All Author List	Xin-Yi Feng, Xi Xu, Yun Zhang, Ye-Min Xu, Qiang She and Bin Deng
Funding Agency and Grant Number	Funding Agency Grant Number The Key Project for Social development of Yangzhou YZ2020069
Corresponding Author	Bin Deng, MD, Associate Professor, Chief Physician, Department of Gastroenterology, Affiliated Hospital of Yangzhou University, No. 368 Hanjiang Middle Road, Yangzhou 225000, Jiangsu Province, China. chinadbin@126.com
Key Words	Artificial intelligence; Convolutional neural network; Endoscopy; Gastric cancer; Deep learning
Core Tip	With the development of new algorithms and big data, great achievements of artificial intelligence (AI) based on deep learning have been made in diagnostic imaging, especially convolutional neural network (CNN). Esophagogastroduodenoscopy (EGD) is currently the most common method for screening and diagnosing gastric cancer (GC). When AI was combined with EGD, the diagnostic efficacy of GC could be improved. Therefore, we review the application and prospect of CNN in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and provide evidences for following researches.
Citation	Feng XY, Xu X, Zhang Y, Xu YM, She Q, Deng B. Application of a convolutional neural network in detecting and classifying gastric cancer. Artif Intell Gastrointest Endosc 2021; 2(3): 71-78

Received	2021-04-27 06:14
Peer-Review Started	2021-04-27 06:15
To Make the First Decision
Return for Revision	2021-04-28 21:50
Revised	2021-05-21 13:02
Second Decision	2021-06-07 12:28
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-06-07 13:25
Articles in Press	2021-06-07 13:25
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-06-16 03:27
Typeset the Manuscript	2021-06-25 07:34

ISSN	2689-7164 (online)
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