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World J Gastroenterol. Oct 21, 2020; 26(39): 5959-5969
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
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
Abstract

Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.

Keywords: Artificial intelligence, Early esophageal cancer, Barrett's esophagus, Esophageal squamous cell carcinoma, Endoscopic diagnosis, Pathological diagnosis

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.