Viswanath YK, Vaze S, Bird R. Application of convolutional neural networks for computer-aided detection and diagnosis in gastrointestinal pathology: A simplified exposition for an endoscopist. Artif Intell Gastrointest Endosc 2020; 1(1): 1-5 [DOI: 10.37126/aige.v1.i1.1]
Corresponding Author of This Article
Yirupaiahgari KS Viswanath, CCST, FRCS, FRCS (Gen Surg), MBBS, Professor, Upper GI Laparoscopic and Endoscopic Unit, James Cook University Hospital, Marton Road, Middlesbrough, Cleveland TS43BW, United Kingdom. keyhole1234@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
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/
Application of convolutional neural networks for computer-aided detection and diagnosis in gastrointestinal pathology: A simplified exposition for an endoscopist
Yirupaiahgari KS Viswanath, Department of Upper GI Laparoscopic and Endoscopic Unit, James Cook University Hospital, Cleveland TS43BW, United Kingdom
Sagar Vaze, University of Oxford, Oxford OX1 2JD, United Kingdom
Richie Bird, Data Science, King's College, London E14 0ST, United Kingdom
Author contributions: Vaze S wrote the convolutional neural network (CNN) section, expert comments, diagrams, proofreading; Bird R expert comments, appraised the process; Viswanath YKS contributed the conceptualization, writing and proofreading of the paper.
Conflict-of-interest statement: Nothing to disclose.
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: Yirupaiahgari KS Viswanath, CCST, FRCS, FRCS (Gen Surg), MBBS, Professor, Upper GI Laparoscopic and Endoscopic Unit, James Cook University Hospital, Marton Road, Middlesbrough, Cleveland TS43BW, United Kingdom. keyhole1234@gmail.com
Received: June 1, 2020 Peer-review started: June 1, 2020 First decision: June 18, 2020 Revised: July 14, 2020 Accepted: July 17, 2020 Article in press: July 17, 2020 Published online: July 28, 2020 Processing time: 51 Days and 19.3 Hours
Abstract
The application of artificial intelligence (AI), especially machine learning or deep learning (DL), is advancing at a rapid pace. The need for increased accuracy at endoscopic visualisation of the gastrointestinal (GI) tract is also growing. Convolutional neural networks (CNNs) are one such model of DL, which have been used for endoscopic image analysis, whereby computer-aided detection and diagnosis of GI pathology can be carried out with increased scrupulousness. In this article, we briefly focus on the framework of the utilisation of CNNs in GI endoscopy along with a short review of a few published AI-based articles in the last 4 years.
Core tip: The convolutional neural network (CNN), a deep learning model, has gained immense success in endoscopy image analysis, with its application to diagnose and detect gastrointestinal (GI) pathology at endoscopy. This article shares a basic framework of the utilisation of CNNs in GI endoscopy, along with a concise review of a few published AI-based endoscopy articles in the last 4 years.
