Kotelevets SM. Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases. World J Clin Oncol 2025; 16(9): 107993 [PMID: 41024848 DOI: 10.5306/wjco.v16.i9.107993]
Corresponding Author of This Article
Sergey M Kotelevets, MD, Professor, Department of Propaedeutics of Internal Medicine, North Caucasus State Academy, Stavropolskaya Street 36, Cherkessk 369000, Karachay-Cherkess Republic, Russia. smkotelevets@mail.ru
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 Clin Oncol. Sep 24, 2025; 16(9): 107993 Published online Sep 24, 2025. doi: 10.5306/wjco.v16.i9.107993
Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases
Sergey M Kotelevets
Sergey M Kotelevets, Department of Propaedeutics of Internal Medicine, North Caucasus State Academy, Cherkessk 369000, Karachay-Cherkess Republic, Russia
Author contributions: Kotelevets SM contributed to this paper, designed the overall concept and outline of the manuscript, contributed to the design of the manuscript, contributed to the writing and editing the manuscript, illustrations, and review of literature.
Conflict-of-interest statement: The author reports 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: Sergey M Kotelevets, MD, Professor, Department of Propaedeutics of Internal Medicine, North Caucasus State Academy, Stavropolskaya Street 36, Cherkessk 369000, Karachay-Cherkess Republic, Russia. smkotelevets@mail.ru
Received: April 2, 2025 Revised: May 22, 2025 Accepted: August 25, 2025 Published online: September 24, 2025 Processing time: 174 Days and 11.8 Hours
Abstract
Serological screening, endoscopic imaging, morphological visual verification of precancerous gastric diseases and changes in the gastric mucosa are the main stages of early detection, accurate diagnosis and preventive treatment of gastric precancer. Laboratory - serological, endoscopic and histological diagnostics are carried out by medical laboratory technicians, endoscopists, and histologists. Human factors have a very large share of subjectivity. Endoscopists and histologists are guided by the descriptive principle when formulating imaging conclusions. Diagnostic reports from doctors often result in contradictory and mutually exclusive conclusions. Erroneous results of diagnosticians and clinicians have fatal consequences, such as late diagnosis of gastric cancer and high mortality of patients. Effective population serological screening is only possible with the use of machine processing of laboratory test results. Currently, it is possible to replace subjective imprecise description of endoscopic and histological images by a diagnostician with objective, highly sensitive and highly specific visual recognition using convolutional neural networks with deep machine learning. There are many machine learning models to use. All machine learning models have predictive capabilities. Based on predictive models, it is necessary to identify the risk levels of gastric cancer in patients with a very high probability.
Core Tip: Prevention of gastric cancer compose of consistent measures to identify precancerous gastric diseases and changes in the gastric mucosa. The first stage is population serological screening for atrophic gastritis. The next stages are endoscopic and morphological visualization of precancerous changes of varying severity. Evaluation of the results of population serological screening is not possible without machine data processing. Accuracy of visualization of endoscopic (macroscopic) and histological (microscopic) images is not possible without the use of convolutional neural networks and deep machine learning. The development and implementation of artificial intelligence will significantly increase the effectiveness of preventive measures.
