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Randomized Controlled Trial
. 2019 Oct;68(10):1813-1819.
doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27.

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Pu Wang  1 , Tyler M Berzin  2 , Jeremy Romek Glissen Brown  2 , Shishira Bharadwaj  2 , Aymeric Becq  2 , Xun Xiao  1 , Peixi Liu  1 , Liangping Li  1 , Yan Song  1 , Di Zhang  1 , Yi Li  1 , Guangre Xu  1 , Mengtian Tu  1 , Xiaogang Liu  1
Affiliations
Randomized Controlled Trial

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Pu Wang et al. Gut. 2019 Oct.

Abstract

Objective: The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR.

Design: In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR.

Results: Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001).

Conclusions: In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost-benefit ratio of such effects has to be determined further.

Trial registration number: ChiCTR-DDD-17012221; Results.

Keywords: colonoscopy; colorectal cancer screening; computerised image analysis.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Deep learning architecture. The detection algorithm is a deep convolutional neural network (CNN) based on SegNet architecture. Data flow is from left to right: a colonoscopy image is sequentially warped into a binary image, with 1 representing polyp pixels and 0 representing no polyp in probability a map. This is then displayed, as showed in the output, with a hollow tracing box on the CADe monitor.
Figure 2
Figure 2
Flow Diagram of Enrollment. 1130 patients met the inclusion criteria, and 72 of them were exclude because of failed procedures, diagnosed IBD or CRC. CRC, colorectal cancer; IBD, inflammatory bowel disease.
See this image and copyright information in PMC

Comment in

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