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Randomized Controlled Trial
. 2021 Oct;10(20):7184-7193.
doi: 10.1002/cam4.4261. Epub 2021 Sep 3.

Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection

Lei Xu  1 , Xinjue He  2 , Jianbo Zhou  3 , Jie Zhang  2 , Xinli Mao  4 , Guoliang Ye  5 , Qiang Chen  6 , Feng Xu  7 , Jianzhong Sang  3 , Jun Wang  4 , Yong Ding  5 , Youming Li  2 , Chaohui Yu  2
Affiliations
Randomized Controlled Trial

Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection

Lei Xu et al. Cancer Med. 2021 Oct.

Abstract

Background: Artificial intelligence (AI) assistance has been considered as a promising way to improve colonoscopic polyp detection, but there are limited prospective studies on real-time use of AI systems.

Methods: We conducted a prospective, multicenter, randomized controlled trial of patients undergoing colonoscopy at six centers. Eligible patients were randomly assigned to conventional colonoscopy (control group) or AI-assisted colonoscopy (AI group). AI assistance was our newly developed AI system for real-time colonoscopic polyp detection. Primary outcome is polyp detection rate (PDR). Secondary outcomes include polyps per positive patient (PPP), polyps per colonoscopy (PPC), and non-first polyps per colonoscopy (PPC-Plus).

Results: A total of 2352 patients were included in the final analysis. Compared with the control, AI group did not show significant increment in PDR (38.8% vs. 36.2%, p = 0.183), but its PPC-Plus was significantly higher (0.5 vs. 0.4, p < 0.05). In addition, AI group detected more diminutive polyps (76.0% vs. 68.8%, p < 0.01) and flat polyps (5.9% vs. 3.3%, p < 0.05). The effects varied somewhat between centers. In further logistic regression analysis, AI assistance independently contributed to the increment of PDR, and the impact was more pronounced for male endoscopists, shorter insertion time but longer withdrawal time, and elderly patients with larger waist circumference.

Conclusion: The intervention of AI plays a limited role in overall polyp detection, but increases detection of easily missed polyps; ChiCTR.org.cn number, ChiCTR1800015607.

Keywords: artificial intelligence; cancer prevention; colorectal polyps; endoscopy; image analysis.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Diagram of study design. Eligible patients were randomly assigned to two groups. In control group, patients underwent a conventional colonoscopy with the AI‐assisted polyp detection system turned off; In AI group, patients underwent a colonoscopy with the AI system running, alerting the endoscopist to the detected polyp by a green indicator box, as well as a “Di” sound. AI, artificial intelligence
FIGURE 2
FIGURE 2
The study flowchart. (1) Screen patients based on inclusion and exclusion criteria. (2) 2488 eligible patients were randomized to the control or AI groups. (3) 1248 patients in control group underwent a conventional colonoscopy, while 1240 patients in AI group underwent a colonoscopy with real‐time use of AI‐assisted polyp detection system. (4) Excluding 136 patients with unqualified colonoscopy or suspected severe intestinal disease, the final statistical analysis was performed. AI, artificial intelligence; CRC, colorectal cancer; IBD, inflammatory bowel disease
FIGURE 3
FIGURE 3
The PDR and polyp size distribution of the control and AI groups in each center. In most centers, the detection of diminutive polyps in AI group showed an increasing trend, and in ZJU Ningbo, the only center with significant improved PDR, there was a significant increase in diminutive polyp detection. *The PDR of control group was significantly higher than that of AI group in ZJU Ningbo center (p < 0.05). AI, artificial intelligence; PDR, polyp detection rate
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