Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Jul 28, 2020; 1(1): 1-4
Published online Jul 28, 2020. doi: 10.35712/aig.v1.i1.1
Digital histology in celiac disease: A practice changer
Daniel Vasile Balaban, Mariana Jinga
Daniel Vasile Balaban, Mariana Jinga, Internal Medicine and Gastroenterology, Carol Davila University of Medicine and Pharmacy, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest 020021, Romania
Author contributions: Balaban DV and Jinga M wrote the manuscript.
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: Daniel Vasile Balaban, MD, PhD, Senior Lecturer, Internal Medicine and Gastroenterology, Carol Davila University of Medicine and Pharmacy, Dr. Carol Davila Central Military Emergency University Hospital, 37 Dionisie Lupu, Bucharest 020021, Romania. vbalaban@yahoo.com
Received: July 1, 2020
Peer-review started: July 1, 2020
First decision: July 15, 2020
Revised: July 18, 2020
Accepted: July 21, 2020
Article in press: July 21, 2020
Published online: July 28, 2020

Artificial intelligence (AI) has grown tremendously in the last decades and is undoubtedly the future era in medicine. Concerning digestive diseases, applications of AI include clinical gastroenterology, gastrointestinal endoscopy and imaging, and not least pathological diagnosis. Several gastrointestinal pathologies require histological confirmation for a positive diagnosis. Among them, celiac disease (CD) diagnosis has been in the spotlight over time, but controversy is still ongoing with regard to the so-called celiac-type histology. Despite efforts to improve histological diagnosis in CD, there are still several issues and pitfalls associated with duodenal histology reading. Several papers have assessed the accuracy of AI techniques in detecting CD on duodenal biopsy images and have shown high diagnostic performance over standard histology reading. We discuss the role of computer-assisted histology in improving the assessment of mucosal architectural injury and inflammation in CD patients, both for diagnosis and follow-up.

Keywords: Celiac disease, Histology, Artificial intelligence, Computer, Digital, Diagnosis

Core tip: Histology in celiac disease (CD) diagnosis is hampered by several pitfalls, from low adherence to biopsy sampling recommendations and reporting of results to significant inter-observer variability. A quantitative, computer-assisted histological assessment of mucosal biopsies could overcome many of the current limitations of conventional histology. We herein discuss the current evidence on artificial intelligence-based histology in CD diagnosis and its role in improving histological measurements in CD.