Tsukanov VV, Vasyutin AV, Kasparov EV, Tonkikh JL. Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy? World J Gastroenterol 2025; 31(22): 106500 [DOI: 10.3748/wjg.v31.i22.106500]
Corresponding Author of This Article
Vladislav V Tsukanov, MD, PhD, Full Professor, Clinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center “Krasnoyarsk Science Center” of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, 3-G Partizan Zheleznyak Street, Krasnoyarsk 660022, Russia. gastro@impn.ru
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
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. Jun 14, 2025; 31(22): 106500 Published online Jun 14, 2025. doi: 10.3748/wjg.v31.i22.106500
Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy?
Vladislav V Tsukanov, Alexander V Vasyutin, Edward V Kasparov, Julia L Tonkikh
Vladislav V Tsukanov, Alexander V Vasyutin, Edward V Kasparov, Julia L Tonkikh, Clinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center “Krasnoyarsk Science Center” of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, Krasnoyarsk 660022, Russia
Author contributions: Tsukanov VV, Vasyutin AV, Kasparov EV and Tonkikh JL contributed to this paper; Tsukanov VV designed the overall concept and outline of the manuscript; Tonkikh JL reviewed the literature; Vasyutin AV wrote the original draft; Tsukanov VV and Kasparov EV reviewed and edited the manuscript; Tsukanov VV, Vasyutin AV and Kasparov EV participated in drafting the manuscript; all authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Vladislav V Tsukanov, MD, PhD, Full Professor, Clinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center “Krasnoyarsk Science Center” of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, 3-G Partizan Zheleznyak Street, Krasnoyarsk 660022, Russia. gastro@impn.ru
Received: February 28, 2025 Revised: April 3, 2025 Accepted: April 22, 2025 Published online: June 14, 2025 Processing time: 104 Days and 20.6 Hours
Abstract
Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the second leading cause of cancer death worldwide. In this regard, CRC screening is one of the most important issues in modern preventive medicine. Colorectal polyps are potential predictors of CRC, and therefore represent one of the leading targets for screening colonoscopy. The difficulty of analyzing the information obtained during colonoscopy, including the size, location, shape, type of polyps, the need to standardize morphological data, determines that recently a number of works have promoted the opinion on the advisability of using various artificial intelligence (AI) methods to improve the effectiveness of endoscopic screening for CRC. At the same time, they point to a number of errors and methodological problems in the use of AI systems for the diagnosis of colorectal polyps. In this regard, the interpretation of the work of Shi et al, devoted to the use of a machine learning-based predictive model for monitoring the results of colorectal polypectomy, is undoubtedly interesting. In our opinion, the prospects for using AI to assess endoscopic screening for CRC look certainly positive, but the road to its widespread use will not be easy.
Core Tip: Endoscopic screening of colorectal polyps is one of the most relevant methods for preventing colorectal cancer. Recently, various artificial intelligence (AI) systems have been used extremely actively to improve the efficiency of polyp diagnostics during colonoscopy. However, until now along with increasing the efficiency of polyp detection and reducing the number of errors during colonoscopy, a number of modern studies note the presence of significant limitations and the possibility of false diagnostics when using AI in real clinical practice. The prospects for using AI for this issue are undeniable, but long-term efforts are required along this road.
