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Cited by 865Colorectal Cancer Screening and DetectionAI in cancer detectionColorectal Cancer Surgical Treatments

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

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Wang, Pu, Berzin, Tyler M, Glissen Brown, Jeremy Romek, Bharadwaj, Shishira, Becq, Aymeric, Xiao, Xun, Liu, Peixi, Li, Liangping +6 more·2019·Gut
DOI 10.1136/gutjnl-2018-317500PMC PMC6839720
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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.

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Authors
Wang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., Li, Y., Xu, G., Tu, M., & Liu, X.
Affiliations
  • Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN)
  • Beth Israel Deaconess Medical Center (US)
Corresponding author
Liu, Xiaogang
ORCID
Venue
Gut
Publisher
BMJ
Published
2019-02-27
Volume
68
Issue
10
Pages
1813-1819
ISSN
  • 0017-5749 (ISSN-L)
  • 1468-3288
DOI
10.1136/gutjnl-2018-317500
PMC
PMC6839720
OpenAlex
W2916105049
Cited by
865
References
4
Citations by year
2026: 96 · 2025: 101 · 2024: 112 · 2023: 111 · 2022: 111 · 2021: 155 · 2020: 151 · 2019: 25 · 2017: 1 · 2014: 1
FWCI
46.19
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Gut (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
Keywords
colonoscopy, polyp detection, adenoma detection rate, computer-aided diagnosis, deep learning, artificial intelligence, diminutive adenomas, hyperplastic polyps, colorectal cancer screening, real-time alarm system
MeSH terms
Adenoma, China, Colonic Polyps, Colonoscopy, Female, Follow-Up Studies, Humans, Male, Middle Aged, Prospective Studies, Time Factors, Colorectal Neoplasms, Reproducibility of Results, Incidence, Early Detection of Cancer

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