Meta-Analysis
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 7, 2025; 31(21): 105753
Published online Jun 7, 2025. doi: 10.3748/wjg.v31.i21.105753
Artificial intelligence for reducing missed detection of adenomas and polyps in colonoscopy: A systematic review and meta-analysis
Sheng-Yu Wang, Jia-Cheng Gao, Shuo-Dong Wu
Sheng-Yu Wang, The Second Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
Jia-Cheng Gao, Department of Orthopedic Surgery, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
Shuo-Dong Wu, Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
Co-first authors: Sheng-Yu Wang and Jia-Cheng Gao.
Author contributions: Wang SL and Gao JC designed the research study; Wang SL, Gao JC, and Wu SD performed the research; Wang SL, Gao JC, and Wu SD analyzed the data and wrote the manuscript; all authors have read and approved the final manuscript.
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: Shuo-Dong Wu, MD, Chief, Professor, Department of General Surgery, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang 110004, Liaoning Province, China. 13909824526@163.com
Received: February 6, 2025
Revised: March 21, 2025
Accepted: May 19, 2025
Published online: June 7, 2025
Processing time: 120 Days and 16.1 Hours
Abstract
BACKGROUND

Colorectal cancer has a high incidence and mortality rate, and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise. In recent years, computer-aided detection (CADe) systems have been increasingly integrated into colonoscopy to improve detection accuracy. However, while most studies have focused on adenoma detection rate (ADR) as the primary outcome, the more sensitive adenoma miss rate (AMR) has been less frequently analyzed.

AIM

To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.

METHODS

A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2, 2024. Statistical analyses were performed to compare outcomes between groups, and potential publication bias was assessed using funnel plots. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations, Assessment, Development, and Evaluation approach.

RESULTS

Five studies comprising 1624 patients met the inclusion criteria. AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group (147/927, 15.9% vs 345/960, 35.9%; P < 0.01). However, CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or ≥ 10 mm. The polyp miss rate (PMR) was also lower in the CADe-assisted group [odds ratio (OR), 0.35; 95% confidence interval (CI): 0.23-0.52; P < 0.01]. While the overall ADR did not differ significantly between groups, the ADR during the first-pass examination was higher in the CADe-assisted group (OR, 1.37; 95%CI: 1.10-1.69; P = 0.004). The level of evidence for the included randomized controlled trials was graded as moderate.

CONCLUSION

CADe can significantly reduce AMR and PMR while improving ADR during initial detection, demonstrating its potential to enhance colonoscopy performance. These findings highlight the value of CADe in improving the detection of colorectal neoplasms, particularly small and histologically distinct adenomas.

Keywords: Artificial intelligence; Computer-aided detection; Colonoscopy; Neoplasms; Prevention and control

Core Tip: Artificial intelligence is being increasingly used in colonoscopy, with more and more studies reporting its potential benefits. However, most studies have focused on adenoma detection rate (ADR) as the primary outcome and assessed only short-term effects. Recently, adenoma miss rate (AMR) has gained more attention, and based on this, we designed this meta-analysis to evaluate the effect of computer-aided detection on AMR, compared it with ADR, and assessed its long-term impact.