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World J Gastroenterol. Feb 14, 2019; 25(6): 672-682
Published online Feb 14, 2019. doi: 10.3748/wjg.v25.i6.672
Artificial intelligence in medical imaging of the liver
Li-Qiang Zhou, Jia-Yu Wang, Song-Yuan Yu, Ge-Ge Wu, Qi Wei, You-Bin Deng, Xing-Long Wu, Xin-Wu Cui, Christoph F Dietrich
Li-Qiang Zhou, Jia-Yu Wang, Ge-Ge Wu, Qi Wei, You-Bin Deng, Xin-Wu Cui, Christoph F Dietrich, Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Song-Yuan Yu, Department of Ultrasound, Tianyou Hospital Affiliated to Wuhan University of Technology, Wuhan 430030, Hubei Province, China
Xing-Long Wu, School of Mathematics and Computer Science, Wuhan Textitle University, Wuhan 430200, Hubei Province, China
Christoph F Dietrich, Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg, Würzburg 97980, Germany
Author contributions: Cui XW established the design and conception of the paper; Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, and Dietrich CF explored the literature data; Zhou LQ provided the first draft of the manuscript, which was discussed and revised critically for intellectual content by Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, and Dietrich CF; all authors discussed the statement and conclusions and approved the final version to be published.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors who contributed their efforts in this manuscript.
Open-Access: 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/
Corresponding author: Xin-Wu Cui, MD, PhD, Professor of Medicine, Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan 430030, Hubei Province, China. cuixinwu@live.cn
Telephone: +86-15927103161 Fax: +86-27-83662640
Received: November 25, 2018
Peer-review started: November 26, 2018
First decision: December 12, 2018
Revised: December 24, 2018
Accepted: January 9, 2019
Article in press: January 9, 2019
Published online: February 14, 2019
Processing time: 83 Days and 2.4 Hours
Abstract

Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.

Keywords: Liver; Imaging; Ultrasound; Artificial intelligence; Machine learning; Deep learning

Core tip: Artificial intelligence (AI) is widely used and gaining in popularity in the medical imaging of the liver. AI can achieve an increased accuracy for diagnosis with higher efficiency and greatly reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning algorithms and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.