Opinion Review
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 21, 2020; 26(39): 5911-5918
Published online Oct 21, 2020. doi: 10.3748/wjg.v26.i39.5911
Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped
Emanuele Sinagra, Matteo Badalamenti, Marcello Maida, Marco Spadaccini, Roberta Maselli, Francesca Rossi, Giuseppe Conoscenti, Dario Raimondo, Socrate Pallio, Alessandro Repici, Andrea Anderloni
Emanuele Sinagra, Francesca Rossi, Giuseppe Conoscenti, Dario Raimondo, Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
Matteo Badalamenti, Marco Spadaccini, Roberta Maselli, Alessandro Repici, Andrea Anderloni, Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
Marcello Maida, Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta 93100, Italy
Socrate Pallio, Endoscopy Unit, AOUP Policlinico G. Martino, Messina 98125, Italy
Author contributions: Sinagra E designed the study; Sinagra E, Badalamenti M, Maida M, Spadaccini M, Maselli R, Rossi F and Conoscenti G wrote the paper; Pallio S, Raimondo D, Anderloni A and Repici A supervised the work.
Conflict-of-interest statement: Authors declare no conflict of interests for 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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Emanuele Sinagra, MD, PhD, Senior Researcher, Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Contrada Pietra Pollastra Pisciotto, Cefalù 90015, Italy. emanuelesinagra83@googlemail.com
Received: July 27, 2020
Peer-review started: July 27, 2020
First decision: August 8, 2020
Revised: August 18, 2020
Accepted: September 23, 2020
Article in press: September 23, 2020
Published online: October 21, 2020
Processing time: 86 Days and 1 Hours
Abstract

Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.

Keywords: Colonoscopy; Artificial intelligence; Adenoma detection rate; Pathology; Endoscopy; Computer-aided detection and diagnosis

Core Tip: Artificial intelligence is an emerging technology which application is rapidly increasingin numerous medical fields. The several applications of artificial intelligence in gastroenterology are showing promising results, especially in the setting of gastrointestinal oncology. Among these, the techniques able to increase the Adenoma detection rate will play a key role in reducing the colorectal cancer incidence and its related mortality caused by undetected or missclassified interval cancers. Furthermore, a significant reduction in costs is also expected. In addition the assistance of the Computer-aided detection and diagnosis systems will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.