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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Wang, P; Berzin, TM; Glissen Brown, JR; Bharadwaj, S; Becq, A; Xiao, X; Liu, P; Li, L; Song, Y; Zhang, D; Li, Y; Xu, G; Tu, M; Liu, X

Published Date

  • October 2019

Published In

Volume / Issue

  • 68 / 10

Start / End Page

  • 1813 - 1819

PubMed ID

  • 30814121

Pubmed Central ID

  • PMC6839720

Electronic International Standard Serial Number (EISSN)

  • 1468-3288

Digital Object Identifier (DOI)

  • 10.1136/gutjnl-2018-317500


  • eng

Conference Location

  • England