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World J Gastroenterol. Jun 28, 2021; 27(24): 3543-3555
Published online Jun 28, 2021. doi: 10.3748/wjg.v27.i24.3543
Usefulness of artificial intelligence in gastric neoplasms
Ji Hyun Kim, Seung-Joo Nam, Sung Chul Park
Ji Hyun Kim, Seung-Joo Nam, Sung Chul Park, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
Author contributions: Kim JH and Park SC wrote the manuscript and made the tables and figures; Nam SJ assisted in drafting and revising the paper.
Conflict-of-interest statement: The authors declare no conflicts of interest.
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: Sung Chul Park, MD, PhD, Associate Professor, Doctor, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Baengnyeong-ro 156, Gangwon-do, Chuncheon 24289, Kangwon Do, South Korea. schlp@hanmail.net
Received: January 25, 2021
Peer-review started: January 25, 2021
First decision: March 29, 2021
Revised: April 9, 2021
Accepted: May 21, 2021
Article in press: May 21, 2021
Published online: June 28, 2021
Processing time: 150 Days and 20.3 Hours
Abstract

Recently, studies in many medical fields have reported that image analysis based on artificial intelligence (AI) can be used to analyze structures or features that are difficult to identify with human eyes. To diagnose early gastric cancer, related efforts such as narrow-band imaging technology are on-going. However, diagnosis is often difficult. Therefore, a diagnostic method based on AI for endoscopic imaging was developed and its effectiveness was confirmed in many studies. The gastric cancer diagnostic program based on AI showed relatively high diagnostic accuracy and could differentially diagnose non-neoplastic lesions including benign gastric ulcers and dysplasia. An AI system has also been developed that helps to predict the invasion depth of gastric cancer through endoscopic images and observe the stomach during endoscopy without blind spots. Therefore, if AI is used in the field of endoscopy, it is expected to aid in the diagnosis of gastric neoplasms and determine the application of endoscopic therapy by predicting the invasion depth.

Keywords: Artificial intelligence; Convolutional neural network; Gastric neoplasm; Esophagogastroduodenoscopy; Diagnosis; Invasion depth

Core Tip: Recently, image analysis based on artificial intelligence (AI) has been applied in the field of diagnostic endoscopy in gastroenterology, and active research is also being conducted on gastric neoplasms. Several studies reported that AI-based early gastric cancer diagnosis and the prediction of invasion depth showed excellent performance and that the differential diagnosis from non-neoplastic lesions including benign gastric ulcers was possible. Therefore, if AI is used in clinical practice, it can be expected to help diagnose gastric neoplasms and determine treatment methods.