The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Artificial intelligence-assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial
Open accessAbstract
BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking. METHODS: This multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45-75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist's experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393). RESULTS: From November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group. CONCLUSIONS: In this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548).
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“The outcomes showed that AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists.”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [23]
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“The CADe group showed a substantially higher ADR (39.9%) compared to the control group (32.4%) (p<0.001).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [23]
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“Overall, the evidence strongly supports the adoption of AI for improving lesion detection during colonoscopy ( Tables 3 and 4 ) [76 , 78-81] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [81]
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Endoscopic diagnosis and treatment of colon cancer: current evidence and future directions
Gong, H., Li, H., Zheng, Z., Zhu, H., & Zhang, Y. (2026) · Frontiers in Surgery · doi:10.3389/fsurg.2026.1816264
The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions
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Metadata
- Authors
- Xu, H., Tang, R. S. Y., Lam, T. Y. T., Zhao, G., Lau, J. Y. W., Liu, Y., Wu, Q., Rong, L., Xu, W., Li, X., Wong, S. H., Cai, S., Wang, J., Liu, G., Ma, T., Liang, X., Mak, J. W. Y., Xu, H., Yuan, P., Cao, T., Li, F., Ye, Z., Shutian, Z., & Sung, J. J. Y.
- Affiliations
- Xiamen University (CN)
- Chinese University of Hong Kong (HK)
- Inner Mongolia People's Hospital (CN)
- Peking University Cancer Hospital (CN)
- Peking University First Hospital (CN)
- First Hospital of Jilin University (CN)
- Nanyang Technological University (SG)
- Corresponding author
- Sung, Joseph J.Y.
- ORCID
- Venue
- Clinical Gastroenterology and Hepatology
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 21
- Issue
- 2
- Pages
- 337-346.e3
- ISSN
- 1542-3565 (ISSN-L)
- OpenAlex
- W4286257050
- Cited by
- 209
- References
- 28
- Citations by year
- 2026: 37 · 2025: 79 · 2024: 54 · 2023: 36
- FWCI
- 17.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Gastroenterology and Hepatology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- 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
- AI-assisted colonoscopy, colorectal cancer screening, adenoma detection rate, polyp detection, asymptomatic population, endoscopist experience, colonoscopy withdrawal time, fecal immunochemical test, bowel preparation, advanced adenoma, Hong Kong, China, Mongolia
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Randomized Controlled Trials as Topic, Early Detection of Cancer
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