Artificial intelligence in liver ultrasound

doi:10.3748/wjg.v28.i27.3398

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 28, Issue 27

This Article

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (14953)

All Articles published online

The chart showing PDF series, WORD series, HTML series, Figures (1-2) series, Tables (1-2) series.

Item

Count

PDF

646

WORD

HTML

10493

Figures (1-2)

785

Tables (1-2)

695

Sum=12684

Publishing Process of This Article

The chart showing Browse series, Download series.

Item

Count

Browse

651

Download

1618

Sum=2269

Jul 21, 2022 (publication date) through Feb 23, 2026

Times Cited of This Article

Times Cited (35)

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

- Full Article with Cover (PDF)
- Full Article (XML)

Minireviews

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 by National 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.

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.

Keywords: Machine learning; Deep learning; Radiomics; Diffuse liver diseases; Focal liver diseases; 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.