Editorial
Copyright ©The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Mar 7, 2015; 21(9): 2577-2581
Published online Mar 7, 2015. doi: 10.3748/wjg.v21.i9.2577
Quantitative image analysis of celiac disease
Edward J Ciaccio, Govind Bhagat, Suzanne K Lewis, Peter H Green
Edward J Ciaccio, Govind Bhagat, Suzanne K Lewis, Peter H Green, Department of Medicine, Celiac Disease Center, Columbia University, New York, NY 10032, United States
Author contributions: All authors contributed to the manuscript.
Conflict-of-interest: The authors have no conflicts of interest.
Open-Access: 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/
Correspondence to: Edward J Ciaccio, PhD, Department of Medicine, Celiac Disease Center, Columbia University, Harkness Pavilion 804, 180 Fort Washington Avenue, New York, NY 10032, United States. ciaccio@columbia.edu
Telephone: +1-212-3055447 Fax: +1-212-3420447
Received: November 26, 2014
Peer-review started: November 26, 2014
First decision: December 11, 2014
Revised: January 4, 2015
Accepted: January 30, 2015
Article in press: January 30, 2015
Published online: March 7, 2015
Processing time: 103 Days and 11.8 Hours
Core Tip

Core tip: Celiac disease is quite common, being present in approximately 1% of the population worldwide. Although it is common, few cases are actually diagnosed because of the lack or screening and diagnostic tools. In this review we discuss currently used quantitative techniques and suggest directions for improved techniques. By using quantitative methods, it is possible to improve outcomes because these methods are automated, unbiased, rapid, and can detect low-level disease that is not evident by visual inspection of endoscopy images. Therefore, it may be possibly to more rapidly diagnose the disease to prevent morbidity by using computerized methods.