Gao X, Braden B. Artificial intelligence in endoscopy: The challenges and future directions. Artif Intell Gastrointest Endosc 2021; 2(4): 117-126 [DOI: 10.37126/aige.v2.i4.117]
Corresponding Author of This Article
Xiaohong Gao, PhD, Full Professor, Department of Computer Science, Middlesex University, The Burroughs, Hendon, London NW4 4BT, United Kingdom. x.gao@mdx.ac.uk
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Artif Intell Gastrointest Endosc. Aug 28, 2021; 2(4): 117-126 Published online Aug 28, 2021. doi: 10.37126/aige.v2.i4.117
Artificial intelligence in endoscopy: The challenges and future directions
Xiaohong Gao, Barbara Braden
Xiaohong Gao, Department of Computer Science, Middlesex University, London NW4 4BT, United Kingdom
Barbara Braden, Translational Gastroenterology Unit, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, United Kingdom
Author contributions: Gao XH and Braden B contributed to the literature research and writing of the manuscript; Both authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors have no interests to declare.
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/
Corresponding author: Xiaohong Gao, PhD, Full Professor, Department of Computer Science, Middlesex University, The Burroughs, Hendon, London NW4 4BT, United Kingdom. x.gao@mdx.ac.uk
Received: May 22, 2021 Peer-review started: May 22, 2021 First decision: June 18, 2021 Revised: June 20, 2021 Accepted: July 15, 2021 Article in press: July 15, 2021 Published online: August 28, 2021 Processing time: 106 Days and 20 Hours
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.
Core Tip: Precancerous changes in the lining of the oesophagus are easily missed during endoscopy as these lesions usually grow flat with only subtle change in colour, surface pattern and microvessel structure. Many factors impair the quality of endoscopy and subsequently the early detection of oesophageal cancer. Artificial intelligence (AI) solutions provide independence from the skills and experience of the operator in lesion recognition. Recent developments have introduced promising AI systems that will support the clinician in recognising, delineating and classifying precancerous and early cancerous changes during the endoscopy of the oesophagus in real-time.
