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Artif Intell Med Imaging. Jun 28, 2020; 1(1): 19-30
Published online Jun 28, 2020. doi: 10.35711/aimi.v1.i1.19
Artificial intelligence in pancreatic disease
Bang-Bin Chen
Bang-Bin Chen, Department of Medical Imaging, National Taiwan University Hospital, Taipei 10016, Taiwan
Bang-Bin Chen, Department of Radiology, College of Medicine, National Taiwan University, Taipei 10016, Taiwan
Author contributions: Chen BB wrote and revised the manuscript.
Supported by grants from the Ministry of Science and Technology (Taiwan), No. 104-2314-B-002-080-MY3 and No. 107-2314-B-002-102-MY3.
Conflict-of-interest statement: No conflict 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: Bang-Bin Chen, MD, Associate Professor, Department of Medical Imaging, National Taiwan University College of Medicine and Hospital, No. 7, Chung-Shan South Road, Taipei 10016, Taiwan. bangbin@gmail.com
Received: June 9, 2020
Peer-review started: June 9, 2020
First decision: June 15, 2020
Revised: June 18, 2020
Accepted: June 20, 2020
Article in press: June 20, 2020
Published online: June 28, 2020
Abstract

In recent years, the application of artificial intelligence (AI) in radiology has been growing rapidly, fueled by the availability of large datasets, advances in computing power, and newly developed algorithms. Progress in AI applied to medical imaging analyses has transformed these images into quantitative data, termed radiomics. When combined with patients’ clinical data, these models, when developed by machine learning, have the potential to improve diagnostic, prognostic, and predictive accuracy. Currently, limited literature is available on the use of radiomics for pancreatic disease. Here, we will review recent studies in the application of AI in a variety of pancreatic diseases, mainly involving lesion detection, tumor characterization, tumor grading, response, and prognosis evaluation. Finally, we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease.

Keywords: Artificial intelligence, Machine learning, Deep learning, Radiomics, Texture analysis, Pancreas

Core tip: The integration of radiomics, clinical data, and advanced machine-learning methodologies will improve diagnostic, prognostic, and predictive accuracy in patients with pancreatic disease, and facilitate clinical decision and management towards precision medicine.