Li ZY, Liu YH, Cai HQ. Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis. World J Gastrointest Surg 2025; 17(11): 109991 [DOI: 10.4240/wjgs.v17.i11.109991]
Corresponding Author of This Article
Hong-Qiao Cai, MD, PhD, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. hongqiaocai@jlu.edu.cn
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Gastroenterology & Hepatology
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Meta-Analysis
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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/
Nov 27, 2025 (publication date) through Nov 25, 2025
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World Journal of Gastrointestinal Surgery
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1948-9366
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Li ZY, Liu YH, Cai HQ. Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis. World J Gastrointest Surg 2025; 17(11): 109991 [DOI: 10.4240/wjgs.v17.i11.109991]
World J Gastrointest Surg. Nov 27, 2025; 17(11): 109991 Published online Nov 27, 2025. doi: 10.4240/wjgs.v17.i11.109991
Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis
Zong-Yang Li, Ya-Hui Liu, Hong-Qiao Cai
Zong-Yang Li, Ya-Hui Liu, Hong-Qiao Cai, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Author contributions: Cai HQ designed the overall concept and outline of the manuscript; Liu YH contributed to the discussion and design of the manuscript; Li ZY contributed to the writing, and editing the manuscript, illustrations, and review of literature.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Hong-Qiao Cai, MD, PhD, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. hongqiaocai@jlu.edu.cn
Received: May 28, 2025 Revised: August 16, 2025 Accepted: September 15, 2025 Published online: November 27, 2025 Processing time: 182 Days and 2.4 Hours
Abstract
BACKGROUND
Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal (UGI) tract cancer. However, traditional endoscopy has certain limitations in detecting precancerous lesions. In contrast, real-time computer-aided detection (CAD) systems enhanced by artificial intelligence (AI) systems, although they may increase unnecessary medical procedures, can provide immediate feedback during examination, thereby improving the accuracy of lesion detection. This article aims to conduct a meta-analysis of the diagnostic performance of CAD systems in identifying precancerous lesions of UGI tract cancer during esophagogastroduodenoscopy (EGD), evaluate their potential clinical application value, and determine the direction for further research.
AIM
To investigate the improvement of the efficiency of EGD examination by the real-time AI-enabled real-time CAD system (AI-CAD) system.
METHODS
PubMed, EMBASE, Web of Science and Cochrane Library databases were searched by two independent reviewers to retrieve literature with per-patient analysis with a deadline up until April 2025. A meta-analysis was performed with R Studio software (R4.5.0). A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.
RESULTS
The initial search identified 802 articles. According to the inclusion criteria, 2113 patients from 10 studies were included in this meta-analysis. The pooled accuracy difference, logarithmic difference of diagnostic odds ratios, sensitivity, specificity and the area under the summary receiver operating characteristic curve (area under the curve) of both AI group and endoscopist group for detecting precancerous lesion were 0.16 (95%CI: 0.12-0.20), -0.19 (95%CI: -0.75-0.37), 0.89 (95%CI: 0.85-0.92, AI group), 0.67 (95%CI: 0.63-0.71, endoscopist group), 0.89 (95%CI: 0.84-0.93, AI group), 0.77 (95%CI: 0.70-0.83, endoscopist group), 0.928 (95%CI: 0.841-0.948, AI group), 0.722 (95%CI: 0.677-0.821, endoscopist group), respectively.
CONCLUSION
The present studies further provide evidence that the AI-CAD is a reliable endoscopic diagnostic tool that can be used to assist endoscopists in detection of precancerous lesions in the UGI tract. It may be introduced on a large scale for clinical application to enhance the accuracy of detecting precancerous lesions in the UGI tract.
Core Tip: This meta-analysis indicates that the artificial intelligence-enabled real-time computer-aided detection system (AI-CAD) system is superior to endoscopists in detecting precancerous lesions of the upper gastrointestinal (UGI) tract. Its sensitivity, specificity, and diagnostic accuracy are higher, which is helpful in improving lesion recognition ability and may reduce the rate of missed diagnoses. These findings support the clinical potential of integrating AI-CAD into routine endoscopy practice to enhance the early detection and prevention of UGI cancers.
