1. Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study.
- Author
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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, and Han, Zelong
- Subjects
COLON polyps ,ARTIFICIAL intelligence ,DEEP learning ,COLORECTAL cancer ,COLONOSCOPY - 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]
- Published
- 2021
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