Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol 2022; 28(27): 3398-3409 [PMID: 36158262 DOI: 10.3748/wjg.v28.i27.3398]
Corresponding Author of This Article
Fan Jiang, MD, Associate Professor, Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, Anhui Province, China. ahultrasound2005@126.com
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. Jul 21, 2022; 28(27): 3398-3409 Published online Jul 21, 2022. doi: 10.3748/wjg.v28.i27.3398
Artificial intelligence in liver ultrasound
Liu-Liu Cao, Mei Peng, Xiang Xie, Gong-Quan Chen, Shu-Yan Huang, Jia-Yu Wang, Fan Jiang, Xin-Wu Cui, Christoph F Dietrich
Liu-Liu Cao, Mei Peng, Xiang Xie, 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
Shu-Yan Huang, Department of Medical Ultrasound, The First People's Hospital of Huaihua, Huaihua 418000, Hunan Province, China
Jia-Yu Wang, Xin-Wu Cui, Department of Medical Ultrasound, Tongji Hospital of 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: Jiang F, Cui XW and Dietrich CF conceived and established the design of the paper; Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Dietrich CF, Jiang F and Cui XW explored the literature data; Cao LL provided the first draft of the manuscript, which was discussed and revised critically for intellectual content by Peng M, Xie X, Dietrich CF, Jiang F and Cui XW; all authors discussed the statements and conclusions, and approved the final version to be published.
Supported byNational Natural Science Foundation of China, No. 82071953; and 2020 Hunan Province Clinical Medical Technology Innovation Guidance Project.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Fan Jiang, MD, Associate Professor, Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, Anhui Province, China. ahultrasound2005@126.com
Received: February 7, 2022 Peer-review started: February 7, 2022 First decision: April 10, 2022 Revised: April 18, 2022 Accepted: June 19, 2022 Article in press: June 19, 2022 Published online: July 21, 2022 Processing time: 160 Days and 22.5 Hours
Abstract
Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.
Core Tip: Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. Currently, there is a need of a comprehensive review to introduce the application of AI based on ultrasound in diffuse and focal liver lesions. In this article, we introduce the application of AI in the assessment of liver fibrosis and nonalcoholic fatty liver and the differentiation of focal liver lesions. In addition, we discuss the performance of AI based on ultrasound in predicting curative effect, prognosis and microvascular invasion in hepatocellular carcinoma. Lastly, we illustrate the future prospect of AI in liver ultrasound.
