Artificial intelligence in endoscopy: the challenges and future directions

Gao, Xiaohong W. ORCID: https://orcid.org/0000-0002-8103-6624 and Braden, Barbara (2021) Artificial intelligence in endoscopy: the challenges and future directions. Artifical Intelligence in Gastrointestinal Endoscopy, 2 (4) . pp. 117-126. ISSN 2689-7164 [Article] (doi:10.37126/aige.v2.i4.117)

[img]
Preview
PDF - Published version (with publisher's formatting)
Available under License Creative Commons Attribution-NonCommercial 4.0.

Download (13MB) | Preview

Abstract

Artificial intelligence based approaches, in particular deep learning, have achieved state-of-the-art performance in medical fields with increasing number of software systems being approved by both Europe and United States. This paper reviews their applications to early detection of oesophageal cancers with a focus on their advantages and pitfalls. The paper concludes with future recommendations towards the development of a real-time, clinical implementable, interpretable and robust diagnosis support systems.

Item Type: Article
Keywords (uncontrolled): Deep learning, Oesophageal cancer, Early detection, Squamous cell cancer, Barrett’s oesophagus
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 33803
Notes on copyright: ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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 and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Useful Links:
Depositing User: Xiaohong Gao
Date Deposited: 10 Sep 2021 09:07
Last Modified: 30 Sep 2021 15:39
URI: https://eprints.mdx.ac.uk/id/eprint/33803

Actions (login required)

View Item View Item

Statistics

Downloads
Activity Overview
6Downloads
13Hits

Additional statistics are available via IRStats2.