Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Jun;9(5):527-533.
doi: 10.1002/ueg2.12108. Epub 2021 Jun 7.

Current status and limitations of artificial intelligence in colonoscopy

Alexander Hann  1 , Joel Troya  1 , Daniel Fitting  1
Affiliations
Review

Current status and limitations of artificial intelligence in colonoscopy

Alexander Hann et al. United European Gastroenterol J. 2021 Jun.

Abstract

Background: Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development.

Aim: This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail.

Methods: A literature search to identify important studies exploring the use of AI in colonoscopy was performed.

Results: This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.

Keywords: colonic polyps; colonoscopy; colorectal neoplasms; computer-assisted; deep learning; diagnosis; endoscopy; gastrointestinal.

PubMed Disclaimer

Conflict of interest statement

The authors Alexander Hann and Daniel Fitting receive public funding from the state government of Baden‐Württemberg, Germany (Funding cluster “Forum Gesundheitsstandort Baden‐Württemberg”) to research and develop artificial intelligence applications for polyp detection in screening colonoscopy.

Figures

FIGURE 1
FIGURE 1
Example of images obtained using a commercial CADe system. Left image: Recorded raw video signal presenting a polyp that was undetected by the endoscopist and highlighted in the middle image by the CADe. Right image: Distraction of the endoscopist by the CADe due to the highlighting of a small mucosal protrusion instead of the arrow marked polyp
See this image and copyright information in PMC

Comment in

References

    1. Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370:1298‐306. - PMC - PubMed
    1. Wadhwa V, Alagappan M, Gonzalez A, Gupta K, Brown JRG, Cohen J, et al. Physician sentiment toward artificial intelligence (AI) in colonoscopic practice: a survey of US gastroenterologists. Endosc Int Open. 2020;8:E1379‐84. - PMC - PubMed
    1. Bisschops R, East JE, Hassan C, Hazewinkel Y, Kamiński MF, Neumann H, et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) guideline—update 2019. Endoscopy. 2019;51:1155‐79. - PubMed
    1. Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, et al. Efficacy of real‐time computer‐aided detection of colorectal neoplasia in a randomized trial. Gastroenterology. 2020;159:512‐20. - PubMed
    1. Liu W‐N, Zhang Y‐Y, Bian X‐Q, Wang L‐J, Yang Q, Zhang X‐D, et al. Study on detection rate of polyps and adenomas in artificial‐intelligence‐aided colonoscopy. Saudi J Gastroenterol. 2020;26:13‐9. - PMC - PubMed

MeSH terms