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iLIVER 2022, 1(4): 252-264 https://doi.org/10.1016/j.iliver.2022.11.001
Review | Open Access | Issue | Published: 16 November 2022

Artificial intelligence-based ultrasound imaging technologies for hepatic diseases

Show Author's Information Longfei Maa,1,  Rui Wanga,1,  Qiong Hea,  Lijie Huanga,  Xingyue Weia,  Xu Lub,  Yanan Dua,  Jianwen Luoa( ),  Hongen Liaoa( )
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China

1 These authors contributed equally to this work.

Keywords:
Artificial intelligence, Ultrasound imaging, Hepatic diseases, Diagnosis and treatment
Cite this article:
Ma L, Wang R, He Q, et al. Artificial intelligence-based ultrasound imaging technologies for hepatic diseases. iLIVER, 2022, 1(4): 252-264. https://doi.org/10.1016/j.iliver.2022.11.001
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Abstract Full text About this article

Ultrasound (US) imaging is a non-invasive, real-time, economical, and convenient imaging modality that has been widely used in diagnosing and treating hepatic diseases. Artificial intelligence (AI) technology can predict or make decisions based on the experience of clinical experts and knowledge obtained from training data. This technology can help clinicians improve the detection efficiency and evaluate hepatic diseases, promote clinical treatment of the liver, and predict the response of the liver after treatment. This review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases. Covered topics include steatosis grading, fibrosis staging, detection of focal liver lesions, US image segmentation, multimodal image registration, and other applications. At present, the field of AI in US imaging is still in its early stages. With the future progress of AI technology, AI-based US imaging can further improve diagnosis, reduce medical costs, and optimize US-based clinical workflow. This technology has broad prospects for application to hepatic diseases.


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About this article

Artificial intelligence-based ultrasound imaging technologies for hepatic diseases

Show Author's information Longfei Maa,1, Rui Wanga,1, Qiong Hea, Lijie Huanga, Xingyue Weia, Xu Lub, Yanan Dua, Jianwen Luoa( ), Hongen Liaoa( )
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China

1 These authors contributed equally to this work.

Abstract

Ultrasound (US) imaging is a non-invasive, real-time, economical, and convenient imaging modality that has been widely used in diagnosing and treating hepatic diseases. Artificial intelligence (AI) technology can predict or make decisions based on the experience of clinical experts and knowledge obtained from training data. This technology can help clinicians improve the detection efficiency and evaluate hepatic diseases, promote clinical treatment of the liver, and predict the response of the liver after treatment. This review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases. Covered topics include steatosis grading, fibrosis staging, detection of focal liver lesions, US image segmentation, multimodal image registration, and other applications. At present, the field of AI in US imaging is still in its early stages. With the future progress of AI technology, AI-based US imaging can further improve diagnosis, reduce medical costs, and optimize US-based clinical workflow. This technology has broad prospects for application to hepatic diseases.

Keywords: Artificial intelligence, Ultrasound imaging, Hepatic diseases, Diagnosis and treatment

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Received: 27 August 2022
Revised: 13 October 2022
Accepted: 01 November 2022
Published: 16 November 2022
Issue date: December 2022

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© 2022 The Author(s). Published by Elsevier Ltd on behalf of Tsinghua University Press.

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The authors thank the reviewers for their constructive suggestions.

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