Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27(29): 4802-4817 [PMID: 34447227 DOI: 10.3748/wjg.v27.i29.4802]
Corresponding Author of This Article
Rishi Pawa, MBBS, Doctor, Department of Internal Medicine, Section of Gastroenterology and Hepatology, Wake Forest Baptist Medical Center, Medical Center Blvd, Winston-Salem, NC 27157, United States. rpawa@wakehealth.edu
Research Domain of This Article
Gastroenterology & Hepatology
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. Aug 7, 2021; 27(29): 4802-4817 Published online Aug 7, 2021. doi: 10.3748/wjg.v27.i29.4802
Artificial intelligence in colonoscopy
Joel Joseph, Ella Marie LePage, Catherine Phillips Cheney, Rishi Pawa
Joel Joseph, Ella Marie LePage, Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
Catherine Phillips Cheney, Department of Internal Medicine, Wake Forest School of Medicine, Winston Salem, NC 27157, United States
Rishi Pawa, Department of Internal Medicine, Section of Gastroenterology and Hepatology, Wake Forest Baptist Medical Center, Winston-Salem, NC 27157, United States
Author contributions: Joseph J provided topic outlining, literature review and original draft preparation; LePage EM performed topic outlining, literature review and original draft preparation; Cheney CP performed literature review and original draft preparation; and Pawa R performed topic outlining, literature review, expertise and manuscript editing.
Conflict-of-interest statement: The authors declare that there are no any conflict of interests.
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: Rishi Pawa, MBBS, Doctor, Department of Internal Medicine, Section of Gastroenterology and Hepatology, Wake Forest Baptist Medical Center, Medical Center Blvd, Winston-Salem, NC 27157, United States. rpawa@wakehealth.edu
Received: January 28, 2021 Peer-review started: January 28, 2021 First decision: May 2, 2021 Revised: May 12, 2021 Accepted: July 16, 2021 Article in press: July 16, 2021 Published online: August 7, 2021 Processing time: 188 Days and 3.2 Hours
Abstract
Colorectal cancer remains a leading cause of morbidity and mortality in the United States. Advances in artificial intelligence (AI), specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps. Conversely, these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place, decreasing the risks that come with unnecessary polypectomies. This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.
Core Tip: The rapidly evolving field of artificial intelligence (AI) has found many applications in the field of colonoscopy. Specifically, we describe the technologies that have been developed to detect and characterize colonic polyps with the goal of real-time analysis as well as minimizing the risks of avoidable polypectomies. Additionally, we discuss some of the future directions of AI in this area including advancements in robotic technology.
