Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Apr 28, 2021; 2(2): 27-41
Published online Apr 28, 2021. doi: 10.35712/aig.v2.i2.27
Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography
Kai-Po Chang, Shih-Huan Lin, Yen-Wei Chu
Kai-Po Chang, Shih-Huan Lin, Yen-Wei Chu, PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
Kai-Po Chang, Department of Pathology, China Medical University Hospital, Taichung 40447, Taiwan
Yen-Wei Chu, Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 40227, Taiwan
Yen-Wei Chu, Institute of Molecular Biology, National Chung Hsing University, Taichung 40227, Taiwan
Yen-Wei Chu, Agricultural Biotechnology Center, National Chung Hsing University, Taichung 40227, Taiwan
Yen-Wei Chu, Biotechnology Center, National Chung Hsing University, Taichung 40227, Taiwan
Yen-Wei Chu, PhD Program in Translational Medicine, National Chung Hsing University, Taichung 40227, Taiwan
Yen-Wei Chu, Rong Hsing Research Center for Translational Medicine, Taichung 40227, Taiwan
Author contributions: Chu YW and Chang KP conceived the idea for the manuscript; Chang KP and Lin SH reviewed the literature and drafted the manuscript; Chu YW drafted and finally approved the manuscript.
Supported by Ministry of Science and Technology, No. 109-2321-B-005-024 and No. 109-2320-B-039-005; National Chung Hsing University and Chung-Shan Medical University, No. NCHU-CSMU 10911; China Medical University Hospital, No. DMR-109-258; and ChangHua Christian Hospital and National Chung Hsing University, No. NCHU-CCH-11006.
Conflict-of-interest statement: Authors declare no conflict of interests for this article.
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: Yen-Wei Chu, PhD, Director, Professor, Institute of Genomics and Bioinformatics, National Chung Hsing University, Kuo Kuang Road, Taichung 40227, Taiwan. ywchu@nchu.edu.tw
Received: January 27, 2021
Peer-review started: January 27, 2021
First decision: March 7, 2021
Revised: March 21, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: April 28, 2021
Processing time: 87 Days and 16.9 Hours
Abstract

Artificial intelligence (AI), particularly the deep learning technology, have been proven influential to radiology in the recent decade. Its ability in image classification, segmentation, detection and reconstruction tasks have substantially assisted diagnostic radiology, and has even been viewed as having the potential to perform better than radiologists in some tasks. Gastrointestinal radiology, an important subspecialty dealing with complex anatomy and various modalities including endoscopy, have especially attracted the attention of AI researchers and engineers worldwide. Consequently, recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology, particularly in the fields for which public databases are available, such as diagnostic abdominal magnetic resonance imaging (MRI) and computed tomography (CT). This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology, with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and CT. For fields where AI is less developed, this review will also explain the difficulty in AI model training and possible strategies to overcome the technical issues. The authors’ insights of possible future development will be addressed in the last section.

Keywords: Artificial intelligence; Deep learning; Image diagnosis; Radiology; Magnetic resonance imaging; Computed tomography

Core Tip: Gastrointestinal radiology is a subspecialty that is important and complex, and is thus a popular subject in artificial intelligence (AI). Recently many deep-learning based diagnosis assistance tool have been developed in gastrointestinal radiology, particularly in diagnostic abdominal magnetic resonance imaging (MRI) and computed tomography (CT). Herein we will review recent advance of AI in gastrointestinal radiology, with a special focus on abdominal MRI and CT. Current difficulty in less-developed fields will be explained as well.