©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 28, 2021; 27(40): 6825-6843
Published online Oct 28, 2021. doi: 10.3748/wjg.v27.i40.6825
Published online Oct 28, 2021. doi: 10.3748/wjg.v27.i40.6825
Emerging artificial intelligence applications in liver magnetic resonance imaging
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 by the 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.
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
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
Core Tip
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.