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World J Gastroenterol. Oct 7, 2020; 26(37): 5606-5616
Published online Oct 7, 2020. doi: 10.3748/wjg.v26.i37.5606
Artificial intelligence technologies for the detection of colorectal lesions: The future is now
Simona Attardo, Viveksandeep Thoguluva Chandrasekar, Marco Spadaccini, Roberta Maselli, Harsh K Patel, Madhav Desai, Antonio Capogreco, Matteo Badalamenti, Piera Alessia Galtieri, Gaia Pellegatta, Alessandro Fugazza, Silvia Carrara, Andrea Anderloni, Pietro Occhipinti, Cesare Hassan, Prateek Sharma, Alessandro Repici
Simona Attardo, Pietro Occhipinti, Department of Endoscopy and Digestive Disease, AOU Maggiore della Carità, Novara 28100, Italy
Viveksandeep Thoguluva Chandrasekar, Madhav Desai, Prateek Sharma, Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
Marco Spadaccini, Roberta Maselli, Antonio Capogreco, Matteo Badalamenti, Piera Alessia Galtieri, Gaia Pellegatta, Alessandro Fugazza, Silvia Carrara, Andrea Anderloni, Alessandro Repici, Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
Marco Spadaccini, Antonio Capogreco, Alessandro Repici, Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
Harsh K Patel, Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, LA 70124, United States
Cesare Hassan, Endoscopy Unit, Nuovo Regina Margherita Hospital, Roma 00153, Italy
Author contributions: Attardo S, Chandrasekar VT equally contributed to this work; Attardo S, Chandrasekar VT and Spadaccini M substantial contributions to conception and design of the study, acquisition of data, or analysis and interpretation of data, drafting the article; Maselli R, Patel HK, Desai M, Capogreco A, Badalamenti M, Galtieri PA, Pellegatta G, Fugazza A, Carrara S, Anderloni A, Occhipinti P, Hassan C, Sharma P and Repici A substantial contributions to conception and design of the study, acquisition of data, or analysis and interpretation of data, making critical revisions related to important intellectual content of the manuscript; and all authors approved the final version of the article to be published.
Conflict-of-interest statement: No conflict 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: Marco Spadaccini, MD, Doctor, Department of Endoscopy, Humanitas Research Hospital, via Manzoni 56, Rozzano 20089, Italy. marco.spadaccini@humanitas.it
Received: June 2, 2020
Peer-review started: June 2, 2020
First decision: June 12, 2020
Revised: June 30, 2020
Accepted: September 16, 2020
Article in press: September 16, 2020
Published online: October 7, 2020
Processing time: 117 Days and 9 Hours
Abstract

Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

Keywords: Endoscopy; Colonoscopy; Screening; Surveillance; Technology; Quality; Artificial intelligence

Core Tip: The use of artificial intelligence (AI) in colonoscopy has been gaining popularity in current times. At first, the efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been tested in ex vivo settings such as still images or videos from colonoscopies. Recent trials have evaluated the real-time efficacy of DCNN-based systems in improving adenoma detection rate and polyp detection rate. In this review we reported all the preliminary ex vivo experiences and summarized the promising results of the initial randomized controlled trials.