El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2(5): 124-132 [DOI: 10.35712/aig.v2.i5.124]
Corresponding Author of This Article
Sarah El-Nakeep, MD, Associate Professor, Gastroenterology and Hepatology Unit, Internal Medicine Department, Faculty of Medicine, AinShams University, Ramsees street, Cairo 11591, Egypt. sarahnakeep@gmail.com
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 Gastroenterol. Oct 28, 2021; 2(5): 124-132 Published online Oct 28, 2021. doi: 10.35712/aig.v2.i5.124
Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations
Sarah El-Nakeep, Mohamed El-Nakeep
Sarah El-Nakeep, Gastroenterology and Hepatology Unit, Internal Medicine Department, Faculty of Medicine, AinShams University, Cairo 11591, Egypt
Mohamed El-Nakeep, Master of Science in Electrical Engineering "Electronics and Communications", Electronics and Electrical Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11736, Egypt
Mohamed El-Nakeep, Bachelor of Science in Electronics and Electrical Communications, Electronics and Communications and Computers Department, Faculty of Engineering, Helwan University, Cairo 11736, Egypt
Author contributions: El-Nakeep S formulated the research question and done the literature search and provided the medical background of the topic; El-Nakeep M provided the technical and engineering and computational background of the topic and the intellectual explanation of the methods of artificial intelligence; Both authors wrote and approved the final submitted draft.
Conflict-of-interest statement: The authors declare they have no conflict of interest.
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: Sarah El-Nakeep, MD, Associate Professor, Gastroenterology and Hepatology Unit, Internal Medicine Department, Faculty of Medicine, AinShams University, Ramsees street, Cairo 11591, Egypt. sarahnakeep@gmail.com
Received: June 6, 2021 Peer-review started: June 6, 2021 First decision: June 23, 2021 Revised: June 26, 2021 Accepted: September 1, 2021 Article in press: September 1, 2021 Published online: October 28, 2021 Processing time: 143 Days and 7.9 Hours
Abstract
This minireview discusses the benefits and pitfalls of machine learning, and artificial intelligence in upper gastrointestinal endoscopy for the detection and characterization of neoplasms. We have reviewed the literature for relevant publications on the topic using PubMed, IEEE, Science Direct, and Google Scholar databases. We discussed the phases of machine learning and the importance of advanced imaging techniques in upper gastrointestinal endoscopy and its association with artificial intelligence.
Core Tip: This minireview aims to explore an important topic; the role of artificial intelligence in upper gastrointestinal (GI) endoscopy detection of cancer. We tried to delineate the most common obstacles encountered when trying to implement artificial intelligence in upper GI endoscopy for cancer detection and characterization. Moreover, we tried to outline the future prospects of this technique, along with its benefits, and uncertainties. This topic summarizes the wide scope for integration of artificial intelligence, between the practicing physicians and the computational engineers and how their collaboration could provide a better healthcare services.
