Minireviews
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Jul 28, 2020; 1(1): 6-18
Published online Jul 28, 2020. doi: 10.37126/aige.v1.i1.6
Emerging artificial intelligence applications in gastroenterology: A review of the literature
Gaetano Cristian Morreale, Emanuele Sinagra, Alessandro Vitello, Endrit Shahini, Erjon Shahini, Marcello Maida
Gaetano Cristian Morreale, Alessandro Vitello, Marcello Maida, Gastroenterology and Endoscopy Unit, S. Elia- M. Raimondi Hospital, Caltanissetta 93100, Italy
Emanuele Sinagra, Gastroenterology and Endoscopy Unit, Fondazione Istituto G. Giglio, Cefalù 90015, Italy
Endrit Shahini, Gastroenterology and Endoscopy Unit, Istituto di Candiolo, FPO-IRCCS, Candiolo (Torino) 93100, Italy
Erjon Shahini, Polytechnic University of Bari, Bari 70126, Italy
Author contributions: Morreale GC and Maida M are guarantors of the integrity of the entire study and contributed to the manuscript drafting and manuscript revision for important intellectual content; all authors contributed to the manuscript editing and had full control over the preparation of the manuscript.
Conflict-of-interest statement: The authors have no proprietary, financial, professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of 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: Marcello Maida, MD, Doctor, Senior Researcher, Gastroenterology and Endoscopy Unit, S. Elia–M. Raimondi Hospital, Via Giacomo Cusmano, 1, Caltanissetta 93100, Italy. marcello.maida@hotmail.it
Received: June 23, 2020
Peer-review started: June 23, 2020
First decision: July 3, 2020
Revised: July 7, 2020
Accepted: July 17, 2020
Article in press: July 17, 2020
Published online: July 28, 2020
Processing time: 30 Days and 3.3 Hours
Abstract

Artificial intelligence (AI) allows machines to provide disruptive value in several industries and applications. Applications of AI techniques, specifically machine learning and more recently deep learning, are arising in gastroenterology. Computer-aided diagnosis for upper gastrointestinal endoscopy has growing attention for automated and accurate identification of dysplasia in Barrett’s esophagus, as well as for the detection of early gastric cancers (GCs), therefore preventing esophageal and gastric malignancies. Besides, convoluted neural network technology can accurately assess Helicobacter pylori (H. pylori) infection during standard endoscopy without the need for biopsies, thus, reducing gastric cancer risk. AI can potentially be applied during colonoscopy to automatically discover colorectal polyps and differentiate between neoplastic and non-neoplastic ones, with the possible ability to improve adenoma detection rate, which changes broadly among endoscopists performing screening colonoscopies. In addition, AI permits to establish the feasibility of curative endoscopic resection of large colonic lesions based on the pit pattern characteristics. The aim of this review is to analyze current evidence from the literature, supporting recent technologies of AI both in upper and lower gastrointestinal diseases, including Barrett's esophagus, GC, H. pylori infection, colonic polyps and colon cancer.

Keywords: Artificial intelligence; Machine learning; Deep learning; Computer-aided diagnosis; Gastroenterology; Endoscopy

Core tip: Artificial intelligence (AI) allows machines to provide disruptive value in a multitude of industries and knowledge domains. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are arising in gastrointestinal endoscopy. Computer-aided diagnosis has been performed during upper gastrointestinal endoscopy for the automated identification of dysplastic lesions in Barrett’s esophagus for preventing esophageal cancer, as well as in lower gastrointestinal endoscopy for detecting colorectal polyps to prevent colorectal cancer. The aim of this review is to investigate current data from the literature, supporting recent technologies of AI both in upper and lower gastrointestinal diseases, including Barrett's esophagus, gastric cancer, Helicobacter pylori infection, colonic polyps and colon cancer.