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Endoscopy
Original article
Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study
  1. Pu Wang1,
  2. Tyler M Berzin2,
  3. Jeremy Romek Glissen Brown2,
  4. Shishira Bharadwaj2,
  5. Aymeric Becq2,
  6. Xun Xiao1,
  7. Peixi Liu1,
  8. Liangping Li1,
  9. Yan Song1,
  10. Di Zhang1,
  11. Yi Li1,
  12. Guangre Xu1,
  13. Mengtian Tu1,
  14. Xiaogang Liu1
  1. 1 Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
  2. 2 Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Xiaogang Liu, Department of Gastroenterology Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China; Gary.samsph{at}gmail.com

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.

  • colonoscopy
  • colorectal cancer screening
  • computerised image analysis

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/gutjnl-2018-317500

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    Footnotes

    • Contributors PW and XL contributed to study concept and design. PW, XL, LL, PL, XX, YS, DZ, GX, MT and YL contributed to acquisition of data. PW, TMB, JRGB, SB and AB contributed to analysis, interpretation of data, drafting of the manuscript and statistical analysis. All authors read and approved the final manuscript.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Disclaimer The CADe system was developed by Shanghai Wision AI Co, Ltd., and was provided free-of-charge for the purposes of this study. Employees in the company were not involved in the clinical trial in any way, including in study design, statistical analysis or manuscript writing

    • Competing interests None declared.

    • Ethics approval This study was approved by the Institutional Review Board of Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital. It was registered with Chinese Clinical Trial Registry (ChiCTR) under identifier ChiCTR-DDD-17012221.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Patient consent for publication Not required.