Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Oct 28, 2021; 2(5): 198-210
Published online Oct 28, 2021. doi: 10.37126/aige.v2.i5.198
Artificial intelligence and early esophageal cancer
Ning Li, Shi-Zhu Jin
Ning Li, Shi-Zhu Jin, Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
Author contributions: Li N wrote the paper and prepared the figures and tables; Jin SZ revised the paper.
Supported by Heilongjiang Province Education Science "13th Five-Year Plan" 2020 Key Project, No. GJB1320190.
Conflict-of-interest statement: The authors declare no conflicts of interest related to this article.
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:
Corresponding author: Shi-Zhu Jin, MD, Chief Physician, Professor, Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, Heilongjiang Province, China.
Received: July 28, 2021
Peer-review started: July 28, 2021
First decision: September 12, 2021
Revised: September 23, 2021
Accepted: October 27, 2021
Article in press: October 27, 2021
Published online: October 28, 2021

The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.

Keywords: Artificial intelligence, Computer-aided diagnosis, Deep learning, Convolutional neural network, Barrett’s esophagus, Early esophageal cancer

Core Tip: The early diagnosis and early treatment of esophageal cancer (EC) have always been a hot spot in clinical medicine research and are of great importance to the prognosis of patients. With continuous improvements in computer technology and the arrival of the era of big data, the artificial intelligence (AI)-assisted endoscopic diagnosis of EC has also flourished. This review mainly introduces the research progress of AI-assisted endoscopy in the diagnosis of Barrett’s esophagus and early EC.