Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 21, 2020; 26(39): 5959-5969
Published online Oct 21, 2020. doi: 10.3748/wjg.v26.i39.5959
Published online Oct 21, 2020. doi: 10.3748/wjg.v26.i39.5959
Artificial intelligence technique in detection of early esophageal cancer
Lu-Ming Huang, Wen-Juan Yang, Zhi-Yin Huang, Cheng-Wei Tang, Jing Li, Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Huang LM wrote the review; Li J and Tang CW designed and revised the manuscript; Huang LM, Yang WJ, and Huang ZY searched and collected the literature; all authors discussed the statement and conclusions and approved the final version to be published.
Supported by Key Research and Development Program of Science and Technology Department of Sichuan Province , No. 2018GZ0088 ; Science & Technology Bureau of Chengdu , China, No. 2017-CY02-00023-GX .
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors who contributed their efforts in this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Jing Li, MD, PhD, Associate Professor, Department of Gastroenterology, West China Hospital Sichuan University, No. 37 Guoxue Lane, Chengdu 610041, Sichuan Province, China. melody224@163.com
Received: July 16, 2020
Peer-review started: July 16, 2020
First decision: August 8, 2020
Revised: August 22, 2020
Accepted: September 4, 2020
Article in press: September 4, 2020
Published online: October 21, 2020
Peer-review started: July 16, 2020
First decision: August 8, 2020
Revised: August 22, 2020
Accepted: September 4, 2020
Article in press: September 4, 2020
Published online: October 21, 2020
Core Tip
Core Tip: The requirement for more efficient methods of detection and characterization of early esophageal cancer (EC) has led to intensive research in the field of artificial intelligence (AI). Thus, application of AI technique in endoscopic detection of early EC is reviewed intensively. Furthermore, pathological and gene diagnosis for early EC as well as its risk stratification is also commented.