Cited by 61Colorectal Cancer Screening and DetectionAI in cancer detectionGenetic factors in colorectal cancer

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study

Abstract

INTRODUCTION: Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting. METHODS: We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience. RESULTS: A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0–75.0), compared with 75.2% (95% CI 71.7–78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9–66.5), compared with 70.3% (95% CI 65.7–74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%. DISCUSSION: The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.

Overview

Integrity signals

Registry

Crossref · 10.14309/ajg.0000000000002282

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 28

  1. Kaminski, 2017, Performance measures for lower gastrointestinal endoscopy: A European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative, Endoscopy, 49, 378, 10.1055/s-0043-103411 · DOI
  2. Rex, 2015, Quality indicators for colonoscopy, Gastrointest Endosc, 81, 31, 10.1016/j.gie.2014.07.058 · DOI · Paper page
  3. Barua, 2021, Artificial intelligence for polyp detection during colonoscopy: A systematic review and meta-analysis, Endoscopy, 53, 277, 10.1055/a-1201-7165 · DOI · Paper page
  4. Gong, 2020, Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): A randomised controlled study, Lancet Gastroenterol Hepatol, 5, 352, 10.1016/S2468-1253(19)30413-3 · DOI · Paper page
  5. Repici, 2020, Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial, Gastroenterology, 159, 512, 10.1053/j.gastro.2020.04.062 · DOI · Paper page
  6. Su, 2020, Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: A prospective randomized controlled study (with videos), Gastrointest Endosc, 91, 415, 10.1016/j.gie.2019.08.026 · DOI · Paper page
  7. Wang, 2019, Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study, Gut, 68, 1813, 10.1136/gutjnl-2018-317500 · DOI · Paper page
  8. Wang, 2020, Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): A double-blind randomised study, Lancet Gastroenterol Hepatol, 5, 343, 10.1016/S2468-1253(19)30411-X · DOI · Paper page
  9. Hassan, 2021, Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: A systematic review and meta-analysis, Gastrointest Endosc, 93, 77, 10.1016/j.gie.2020.06.059 · DOI · Paper page
  10. Kandel, 2019, Should we resect and discard low risk diminutive colon polyps, Clin Endosc, 52, 239, 10.5946/ce.2018.136 · DOI
  11. Neumann, 2018, Leaving colorectal polyps in place can be achieved with high accuracy using blue light imaging (BLI), United European Gastroenterol J, 6, 1099, 10.1177/2050640618769731 · DOI
  12. von Renteln, 2018, Simplifying resect and discard strategies for real-time assessment of diminutive colorectal polyps, Clin Gastroenterol Hepatol, 16, 706, 10.1016/j.cgh.2017.11.036 · DOI
  13. Ang, 2021, Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged?, Artif Intell Gastrointest Endosc, 2, 36, 10.37126/aige.v2.i2.36 · DOI
  14. Bang, 2021, Computer-aided diagnosis of diminutive colorectal polyps in endoscopic images: Systematic review and meta-analysis of diagnostic test accuracy, J Med Internet Res, 23, e29682, 10.2196/29682 · DOI
  15. Lui, 2020, Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: A systematic review and meta-analysis, Gastrointest Endosc, 92, 11, 10.1016/j.gie.2020.02.033 · DOI
  16. Zorron Cheng Tao Pu, 2020, Computer-aided diagnosis for characterization of colorectal lesions: Comprehensive software that includes differentiation of serrated lesions, Gastrointest Endosc, 92, 891, 10.1016/j.gie.2020.02.042 · DOI
  17. Cao, 2022, Effect of oral simethicone on the quality of colonoscopy: A systematic review and meta-analysis of randomized controlled trials, J Dig Dis, 23, 134, 10.1111/1751-2980.13084 · DOI
  18. Yeh, 2019, The benefit of adding oral simethicone in bowel preparation regimen for the detection of colon adenoma: A systematic review and meta-analysis, J Gastroenterol Hepatol, 34, 830, 10.1111/jgh.14508 · DOI
  19. Ang, 2019, A prospective randomized study of colonoscopy using blue laser imaging and white light imaging in detection and differentiation of colonic polyps, Endosc Int Open, 7, E1207, 10.1055/a-0982-3111 · DOI
  20. Yoshida, 2014, The ability of a novel blue laser imaging system for the diagnosis of invasion depth of colorectal neoplasms, J Gastroenterol, 49, 73, 10.1007/s00535-013-0772-7 · DOI
  21. Rex, 2011, The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps, Gastrointest Endosc, 73, 419, 10.1016/j.gie.2011.01.023 · DOI · Paper page
  22. Hassan, 2010, A resect and discard strategy would improve cost-effectiveness of colorectal cancer screening, Clin Gastroenterol Hepatol, 8, 865, 10.1016/j.cgh.2010.05.018 · DOI
  23. Vleugels, 2019, Diminutive polyps with advanced histologic features do not increase risk for metachronous advanced colon neoplasia, Gastroenterology, 156, 623, 10.1053/j.gastro.2018.10.050 · DOI
  24. Hassan, 2022, Artificial intelligence allows leaving-in-situ colorectal polyps, Clin Gastroenterol Hepatol, 20, 2505, 10.1016/j.cgh.2022.04.045 · DOI · Paper page
  25. Rondonotti, 2023, Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: The Artificial intelligence BLI Characterization (ABC) study, Endoscopy, 55, 14, 10.1055/a-1852-0330 · DOI · Paper page
  26. Gupta, 2020, Spotlight: US Multi-Society Task Force on Colorectal Cancer recommendations for follow-up after colonoscopy and polypectomy, Gastroenterology, 158, 1154, 10.1053/j.gastro.2020.02.014 · DOI
  27. Hassan, 2020, Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) guideline: Update 2020, Endoscopy, 52, 687, 10.1055/a-1185-3109 · DOI
  28. Koo, 2021, Key tips for using computer-aided diagnosis in colonoscopy - observations from two different platforms, Endoscopy, 54, 1018

Cited by

Sorted by citations

See all 61

Metadata

Authors
Li, J. W., Wu, C. C. H., Lee, J. W. J., Liang, R., Soon, G. S. T., Wang, L. M., Koh, X. H., Koh, C. J., Chew, W. D., Lin, K. W., Thian, M. Y., Matthew, R., Kim, G., Khor, C. J. L., Fock, K. M., Ang, T. L., So, J. B. Y., & on behalf of the Artificial Intelligence in Gastrointestinal Endoscopy Singapore (AIGES) Study Group
Affiliations
  • SingHealth (SG)
  • National University of Singapore (SG)
  • Tan Tock Seng Hospital (SG)
  • National University Hospital (SG)
  • Changi General Hospital (SG)
Corresponding author
Li, James Weiquan
Venue
American Journal of Gastroenterology
Published
2023-04-11
Volume
118
Issue
8
Pages
1353-1364
ISSN
  • 0002-9270 (ISSN-L)
  • 1572-0241
OpenAlex
W4364377313
Cited by
61
References
2
Citations by year
2026: 11 · 2025: 23 · 2024: 21 · 2023: 6
FWCI
7.15
Type
journal article
Language
en
Open access
closed
Locations
  • The American Journal of Gastroenterology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Genetic factors in colorectal cancer
Keywords
computer-aided diagnosis, colonoscopy, colorectal polyps, polyp histology prediction, neoplastic polyps, diagnostic accuracy, interobserver agreement, bowel preparation, polyp size, endoscopist experience
MeSH terms
Colonic Polyps, Colonoscopy, Computers, Humans, Predictive Value of Tests, Prospective Studies, Colorectal Neoplasms, Narrow Band Imaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review