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Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study.

Authors :
Luo, Yuchen
Zhang, Yi
Liu, Ming
Lai, Yihong
Liu, Panpan
Wang, Zhen
Xing, Tongyin
Huang, Ying
Li, Yue
Li, Aiming
Wang, Yadong
Luo, Xiaobei
Liu, Side
Han, Zelong
Source :
Journal of Gastrointestinal Surgery; Aug2021, Vol. 25 Issue 8, p2011-2018, 8p
Publication Year :
2021

Abstract

Background and aims: Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods: The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results: In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions: A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration: clinicaltrials.gov Identifier: NCT047126265 [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1091255X
Volume :
25
Issue :
8
Database :
Complementary Index
Journal :
Journal of Gastrointestinal Surgery
Publication Type :
Academic Journal
Accession number :
151648752
Full Text :
https://doi.org/10.1007/s11605-020-04802-4