Almomani A, Hitawala A, Abureesh M, Qapaja T, Alshaikh D, Zmaili M, Saleh MA, Alkhayyat M. Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up. Artif Intell Gastroenterol 2021; 2(3): 85-93 [DOI: 10.35712/aig.v2.i3.85]
Corresponding Author of This Article
Motasem Alkhayyat, MD, Doctor, Department of Internal Medicine, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, United States. alkhaym@ccf.org
Research Domain of This Article
Medicine, General & Internal
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. Jun 28, 2021; 2(3): 85-93 Published online Jun 28, 2021. doi: 10.35712/aig.v2.i3.85
Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up
Ashraf Almomani, Asif Hitawala, Mohammad Abureesh, Thabet Qapaja, Dana Alshaikh, Mohammad Zmaili, Mohannad Abou Saleh, Motasem Alkhayyat
Ashraf Almomani, Asif Hitawala, Department of Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, United States
Mohammad Abureesh, Department of Internal Medicine, Staten Island University Hospital, New York City, NY 10305, United States
Thabet Qapaja, Mohammad Zmaili, Motasem Alkhayyat, Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
Dana Alshaikh, School of Medicine, Mutah University, Alkarak 61710, Jordan
Mohannad Abou Saleh, Department of Gastroenterology and Hepatology, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
Author contributions: Almomani A, Hitawala A, Abureesh M, Qapaja T, Alshaikh D, and Zmaili M formulated the initial draft; Saleh MA, and Alkhayyat M critically revised the manuscript.
Conflict-of-interest statement: No conflicts 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: Motasem Alkhayyat, MD, Doctor, Department of Internal Medicine, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, United States. alkhaym@ccf.org
Received: April 25, 2021 Peer-review started: April 25, 2021 First decision: May 7, 2021 Revised: May 18, 2021 Accepted: June 28, 2021 Article in press: June 28, 2021 Published online: June 28, 2021
Abstract
Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.
Core Tip: There has been a substantial progress made in artificial intelligence in gastroenterology including inflammatory bowel disease. Machine learning would play a major role in predicting disease flare up, response to treatment and overall patient' prognosis.
