Viscaino M, Torres Bustos J, Muñoz P, Auat Cheein C, Cheein FA. Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World J Gastroenterol 2021; 27(38): 6399-6414 [PMID: 34720530 DOI: 10.3748/wjg.v27.i38.6399]
Corresponding Author of This Article
Fernando Auat Cheein, PhD, Associate Professor, Department of Electronic Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso 2340000, Chile. fernando.auat@usm.cl
Research Domain of This Article
Engineering, Biomedical
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Gastroenterol. Oct 14, 2021; 27(38): 6399-6414 Published online Oct 14, 2021. doi: 10.3748/wjg.v27.i38.6399
Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions
Michelle Viscaino, Javier Torres Bustos, Pablo Muñoz, Cecilia Auat Cheein, Fernando Auat Cheein
Michelle Viscaino, Javier Torres Bustos, Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria, Valpaiso 2340000, Chile
Pablo Muñoz, Hospital Clinico, University of Chile, Santiago 8380456, Chile
Cecilia Auat Cheein, Facultad de Medicina, Universidad Nacional de Santiago del Estero, Santiago del Estero 4200, Argentina
Fernando Auat Cheein, Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaiso 2340000, Chile
Author contributions: Viscaino M performed the majority of the writing and prepared the figures and tables; Torres Bustos J performed the writing; Muñoz P provided the medical input in writing the paper; Auat Cheein C performed the writing and made critical revisions related to the medical content of the manuscript; Auat Cheein F designed the outline, edited, and reviewed the final version of the article and managed the funding; all authors read and approved the final manuscript.
Supported byChilean National Agency for Research and Development (ANID), No. FB0008; and CONICYT-PCHA/Doctorado Nacional, No. 2018-21181420.
Conflict-of-interest statement: The authors deny any 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: Fernando Auat Cheein, PhD, Associate Professor, Department of Electronic Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso 2340000, Chile. fernando.auat@usm.cl
Received: February 28, 2021 Peer-review started: February 28, 2021 First decision: March 27, 2021 Revised: April 26, 2021 Accepted: September 14, 2021 Article in press: September 14, 2021 Published online: October 14, 2021 Processing time: 225 Days and 9.4 Hours
Abstract
Colorectal cancer (CRC) was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants. It can be prevented if glandular tissue (adenomatous polyps) is detected early. Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps. However, it has some limitations that include the high polyp miss rate for smaller (< 10 mm) or flat polyps, which are easily missed during visual inspection. Due to the rapid advancement of technology, artificial intelligence (AI) has been a thriving area in different fields, including medicine. Particularly, in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC. This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps, as well as an insightful analysis of the main advantages and misconceptions in the field.
Core Tip: Artificial intelligence-based (AI) methods have demonstrated high performance in classification, object detection, and segmentation tasks. Through multidisciplinary and collaborative work between clinicians and technicians, the advantages of AI have been successfully applied in automatic polyp detection and classification. The new AI-based systems present a better polyp detection rate and contribute to better clinical decision-making for preventing colorectal cancer (CRC). This article provides an overview of recent research focusing on AI and its applications in the early detection of CRC and adenomatous polyps.
