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In Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract

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.

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

2020-Sep-23

Artificial intelligence, Colonoscopy, Computer-aided diagnose