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 Jan;33(2):242-253.
doi: 10.1111/den.13888. Epub 2020 Dec 19.

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy

Pieter Sinonquel  1   2 , Tom Eelbode  3   4 , Peter Bossuyt  1   5 , Frederik Maes  3   4 , Raf Bisschops  1   2
Affiliations
Review

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy

Pieter Sinonquel et al. Dig Endosc. 2021 Jan.

Abstract

Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.

Keywords: artificial intelligence; endoscopy; lower gastrointestinal tract; quality; upper gastrointestinal tract.

PubMed Disclaimer

References

    1. Esteva A, Kuprel B, Novoa RA et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-8.
    1. Baker JA, Rosen EL, Lo JY et al. Computer-aided detection (CAD) in screening mammography: Sensitivity of commercial CAD systems for detection architectural distortion. Am J Roentgenol 2003; 181: 1083-8.
    1. Gulshan V, Peng L, Coram M et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402-10.
    1. Van Der Sommen F, Zinger S, Curvers WL et al. Computer-aided detection of early neoplastic lesions in Barrett's esophagus. Endoscopy 2016; 48: 617-24.
    1. Urban G, Tripathi P, Alkayali T et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy of the HHS public access. Gastroenterology 2018; 155: 1069-78.

LinkOut - more resources