Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials
Abstract
Background This meta-analysis aimed to determine whether artificial intelligence (AI) improves colonoscopy outcome metrics i.e. adenoma detection rate (ADR) and polyp detection rate (PDR). Methods Two authors independently searched Web of Science, PubMed, Science Direct, and Cochrane Library to find all published research before July 2021 that has compared AI-aided colonoscopy with routine colonoscopy (RC) for detection of adenoma and polyp. Results This meta-analysis included 10 RCTs with 6629 individuals in AI-aided (n = 3300) and routine (n = 3329) groups. The results showed that both ADR (RR, 1.43; P Conclusions AI-aided polyp detection system significantly increases lesion detection rate. In addition, lesion detection by AI is hardly affected by factors such as size, location, and shape.
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CADe systems prove particularly beneficial in aiding the detection of small and flat lesions.
“CADe systems prove particularly beneficial in aiding the detection of these challenging lesions [76 , 82 , 83] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [83]
Cites
Adenoma detection rate and risk of colorectal cancer and death
Corley, D. A., Jensen, C. D., Marks, A. R., Zhao, W. K., Lee, J. K., Doubeni, C. A., Zauber, A. G., de Boer, J., Fireman, B. H., Schottinger, J. E., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1309086 · Cited by 2,270 · see citing works
Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy
Huang, J., Liu, W.-N., Zhang, Y.-Y., Bian, X.-Q., Wang, L.-J., Yang, Q., & Zhang, X.-D. (2020) · Saudi Journal of Gastroenterology · doi:10.4103/sjg.sjg_377_19 · Cited by 179 · see citing works
Self-citationXu, Y., Ding, W., Wang, Y., Tan, Y., Xi, C., Ye, N., Wu, D., & Xu, X. (2021) · PLOS ONE · doi:10.1371/journal.pone.0246892 · Cited by 42 · see citing works
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Enhancing human-AI collaboration: The case of colonoscopy
Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., & Reverberi, C. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2023.10.018 · Cited by 28 · see citing works
- Authors
- Huang, D., Shen, J., Hong, J., Zhang, Y., Dai, S., Du, N., Zhang, M., & Guo, D.
- Affiliations
- Zhejiang Chinese Medical University (CN)
- Ningbo No. 2 Hospital (CN)
- Corresponding author
- Guo, Daxin
- Venue
- International Journal of Colorectal Disease
- Published
- 2021-11-11
- Volume
- 37
- Issue
- 3
- Pages
- 495-506
- ISSN
- 0179-1958 (ISSN-L)
- 1432-1262
- OpenAlex
- W3213426728
- Cited by
- 64
- References
- 3
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 12 · 2023: 23 · 2022: 4
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Colorectal Disease (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
- Keywords
- artificial intelligence, adenoma detection rate, polyp detection rate, colorectal adenoma, colorectal polyps, sessile serrated lesions, colonoscopy withdrawal time, colorectal cancer screening
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Randomized Controlled Trials as Topic
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