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World J Gastroenterol. Sep 14, 2021; 27(34): 5715-5726
Published online Sep 14, 2021. doi: 10.3748/wjg.v27.i34.5715
Artificial intelligence for hepatitis evaluation
Wei Liu, Xue Liu, Mei Peng, Gong-Quan Chen, Peng-Hua Liu, Xin-Wu Cui, Fan Jiang, Christoph F Dietrich
Wei Liu, Xue Liu, Mei Peng, Fan Jiang, Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
Gong-Quan Chen, Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
Peng-Hua Liu, Department of Medical Ultrasound, The First Affiliated Hospital of Shaoyang University, Shaoyang 422000, Hunan Province, China
Xin-Wu Cui, 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
Christoph F Dietrich, Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3626, Switzerland
Author contributions: Cui XW, Jiang F, and Dietrich CF established the design and conception of the paper; Liu W, Liu X, Peng M, Chen GQ, Liu PH, Jiang F, Cui XW, and Dietrich CF explored the literature data; Liu W provided the first draft of the manuscript, which was discussed and revised critically for intellectual content by Liu X, Peng M, Chen GQ, Liu PH, Jiang F, Cui XW, and Dietrich CF; all authors discussed the statement and conclusions and approved the final version to be published.
Supported by the National Natural Science Foundation of China, No. 82071953; and Department of Science and Technology of Hunan Province, No. 2020SK52103.
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 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: Xin-Wu Cui, MD, PhD, Professor, 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
Received: March 1, 2021
Peer-review started: March 1, 2021
First decision: April 18, 2021
Revised: April 28, 2021
Accepted: August 2, 2021
Article in press: August 2, 2021
Published online: September 14, 2021
Abstract

Recently, increasing attention has been paid to the application of artificial intelligence (AI) to the diagnosis of diverse hepatic diseases, which comprises traditional machine learning and deep learning. Recent studies have shown the possible value of AI based data mining in predicting the incidence of hepatitis, classifying the different stages of hepatitis, diagnosing or screening for hepatitis, forecasting the progression of hepatitis, and predicting response to antiviral drugs in chronic hepatitis C patients. More importantly, AI based on radiology has been proven to be useful in predicting hepatitis and liver fibrosis as well as grading hepatocellular carcinoma (HCC) and differentiating it from benign liver tumors. It can predict the risk of vascular invasion of HCC, the risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis, and the risk of liver failure after hepatectomy in HCC patients. In this review, we summarize the application of AI in hepatitis, and identify the challenges and future perspectives.

Keywords: Machine learning, Deep learning, Radiomics, Hepatitis, Fibrosis, Hepatocellular carcinoma

Core Tip: Currently, there is a need of a comprehensive review to introduce the applications of artificial intelligence (AI) in hepatitis. We first discuss the possible value of AI based data mining in predicting the incidence of hepatitis, classifying the different stages of hepatitis, diagnosing or screening for hepatitis, forecasting the progression of hepatitis, and predicting response to antiviral drugs in chronic hepatitis C patients. In addition, we introduce the applications of AI based on radiology in predicting hepatitis and liver fibrosis, grading hepatocellular carcinoma (HCC) and differentiating it from benign liver tumors, and predicting the risk of vascular invasion of HCC as well as the risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis.