Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27(40): 6825-6843 [PMID: 34790009 DOI: 10.3748/wjg.v27.i40.6825]
Corresponding Author of This Article
Michael Pavlides, BSc, DPhil, MBBS, MRCP, Consultant Physician-Scientist, Doctor, Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Level 0, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, United Kingdom. michael.pavlides@cardiov.ox.ac.uk
Research Domain of This Article
Gastroenterology & Hepatology
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/
World J Gastroenterol. Oct 28, 2021; 27(40): 6825-6843 Published online Oct 28, 2021. doi: 10.3748/wjg.v27.i40.6825
Emerging artificial intelligence applications in liver magnetic resonance imaging
Charles E Hill, Luca Biasiolli, Matthew D Robson, Vicente Grau, Michael Pavlides
Charles E Hill, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom
Luca Biasiolli, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
Matthew D Robson, MR Physics, Perspectum Ltd, Oxford OX4 2LL, United Kingdom
Vicente Grau, Department of Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
Michael Pavlides, Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
Michael Pavlides, Translational Gastroenterology Unit, University of Oxford, Oxford OX3 9DU, United Kingdom
Michael Pavlides, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, United Kingdom
Author contributions: Hill CE did the literature search and drafted the manuscript; all other authors revised the manuscript for important intellectual content.
Supported bythe Engineering and Physical Sciences Research Council and Medical Research Council, No. EP/L016052/1.
Conflict-of-interest statement: Pavlides M is a shareholder for the company Perspectum Ltd. and has applied for a patent for medical imaging; All other authors declare no conflicts of interest; Robson M is an employee and shareholder for the company Perspectum Ltd.; Hill CE is partially funded by the company Perspectum Ltd.
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: Michael Pavlides, BSc, DPhil, MBBS, MRCP, Consultant Physician-Scientist, Doctor, Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Level 0, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, United Kingdom. michael.pavlides@cardiov.ox.ac.uk
Received: February 6, 2021 Peer-review started: February 6, 2021 First decision: March 29, 2021 Revised: April 16, 2021 Accepted: September 26, 2021 Article in press: September 30, 2021 Published online: October 28, 2021 Processing time: 262 Days and 13.8 Hours
Abstract
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.
Core Tip: Artificial Intelligence (AI) algorithms are becoming increasingly prevalent in magnetic resonance imaging (MRI) after their proven success in computer vision tasks. With regards to liver MRI, these methods have been shown to be successful in tasks from hepatocellular carcinoma detection, to motion reduction to improve undiagnostic scans. They have also been shown in some cases to outperform radiographer level performance. The widespread use of these techniques could positively aid clinicians for years to come, if implemented properly into clinical workflows.
