Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Dec 7, 2018; 24(45): 5057-5062
Published online Dec 7, 2018. doi: 10.3748/wjg.v24.i45.5057
Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy
Thomas de Lange, Pål Halvorsen, Michael Riegler
Thomas de Lange, Department of Transplantation, Oslo University Hospital, Oslo 0424, Norway
Thomas de Lange, Institute of Clinical Medicine, University of Oslo, Oslo 0316, Norway
Pål Halvorsen, Michael Riegler, Center for Digital Engineering Simula Metropolitan, Fornebu 1364, Norway
Pål Halvorsen, Michael Riegler, Department for Informatics, University of Oslo, Oslo 0316, Norway
Author contributions: de Lange T, Halvorsen P and Riegler M contributed to the concept and design of the editorial, drafting of the manuscript and final approval of the manuscript.
Supported by the grants from Norwegian Research Council, No. 282315.
Correspondence author to: Thomas de Lange, MD, PhD, Associate Professor, Department of transplantation, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Postboks 4956 Nydalen, Oslo 0424, Norway. t.d.lange@medisin.uio.no
Telephone: +47-22118080
Received: September 7, 2018
Peer-review started: September 7, 2018
First decision: October 4, 2018
Revised: October 25, 2018
Accepted: November 2, 2018
Article in press: November 2, 2018
Published online: December 7, 2018
Core Tip

Core tip: Assisted diagnosis using artificial intelligence and recent developments in computer hardware have enabled the narrower area of machine learning to equip the endoscopists with potentially powerful tools for computer assisted diagnosis systems. The success depends on various factors; optimizing algorithms, image database quality and size and comparison with existing systems.