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Review
. 2022 Jul 21;28(27):3398-3409.
doi: 10.3748/wjg.v28.i27.3398.

Artificial intelligence in liver ultrasound

Liu-Liu Cao  1 , Mei Peng  1 , Xiang Xie  1 , Gong-Quan Chen  2 , Shu-Yan Huang  3 , Jia-Yu Wang  4 , Fan Jiang  5 , Xin-Wu Cui  4 , Christoph F Dietrich  6
Affiliations
Review

Artificial intelligence in liver ultrasound

Liu-Liu Cao et al. World J Gastroenterol. .

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

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Conflict of interest statement

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

Figures

Figure 1
Figure 1
Main structure of this review. AI: Artificial intelligence; FLLs: Focal liver lesions; HCC: Hepatocellular carcinoma.
Figure 2
Figure 2
Illustration of the flowchart of the application of deep learning and radiomics in focal liver lesions. These two methods were based on big data, which contained image preprocessing, feature extraction and model construction.
See this image and copyright information in PMC

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