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

Yuchen Luo, Yi Zhang, Ming Liu, Yihong Lai, Panpan Liu, Zhen Wang, Tongyin Xing, Ying Huang, Yue Li, Aiming Li, Yadong Wang, Xiaobei Luo, Side Liu, Zelong Han, Yuchen Luo, Yi Zhang, Ming Liu, Yihong Lai, Panpan Liu, Zhen Wang, Tongyin Xing, Ying Huang, Yue Li, Aiming Li, Yadong Wang, Xiaobei Luo, Side Liu, Zelong Han

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.

Trial registration: ClinicalTrials.gov NCT04126265.

Keywords: Artificial intelligence; Colonoscopy; Computer-aided diagnose.

Conflict of interest statement

The authors declare that they have no conflicts of interest.

© 2020. The Author(s).

Figures

Fig. 1
Fig. 1
Flow diagram of enrollment. A total of 150 patients were analyzed, of whom 72 underwent AI-assisted colonoscopy first and 78 underwent traditional colonoscopy first
Fig. 2
Fig. 2
Host device of the real-time polyp detection system
Fig. 3
Fig. 3
a and b Identification of polyps by traditional colonoscopy. c and d Blue box that appears when a polyp is identified by AI-assisted colonoscopy
Fig. 4
Fig. 4
Artificial intelligence (AI) system. The detection algorithm is a deep convolutional neural network (CNN) based on the YOLO network architecture
Fig. 5
Fig. 5
a and b Feces mistakenly identified by AI. c Mucosal fold mistakenly identified by AI. d Ileocecal lobe misrecognized by AI

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Source: PubMed

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