Early screening strategies for metabolic associated fatty liver disease

Kaiye HUA; Mengfan JIA; Yingwei ZHU; Zhonghua LU; Jian LU; Hong TANG

doi:10.12449/JCH260223

Volume 42 Issue 2

Feb. 2026

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Article Navigation > Journal of Clinical Hepatology > 2026 > 42(2): 420-426

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Early screening strategies for metabolic associated fatty liver disease

DOI: 10.12449/JCH260223

Kaiye HUA^{1, 2},
Mengfan JIA^{1, 2},
Yingwei ZHU^{1, 2
,
,
,},
Zhonghua LU³,
Jian LU²,
Hong TANG⁴

1.
Wuxi School of Medicine，Jiangnan University，Wuxi，Jiangsu 214122，China
2.
Department of Gastroenterology，Affiliated Central Hospital of Jiangnan University （Wuxi No. 2 People’s Hospital），Wuxi，Jiangsu 214002，China
3.
Department of Hepatology，Wuxi Fifth People’s Hospital，Wuxi，Jiangsu 214061，China
4.
Department of Pathology，Affiliated Central Hospital of Jiangnan University （Wuxi No. 2 People’s Hospital），Wuxi，Jiangsu 214002，China

Research funding:

“Light of Taihu Lake” Science and Technology Research Project Sponsored by Science and Technology Bureau of Wuxi City (Y20232022);

Medical Key Discipline Program of Wuxi Health Commission (Z202415)

More Information

Corresponding author: ZHU Yingwei， zhuyingw2020@163.com （ORCID： 0000-0002-8636-7318）
Received Date: 2025-06-30
Accepted Date: 2025-09-11
Published Date: 2026-02-25

Abstract

Abstract

Metabolic associated fatty liver disease （MAFLD） is a common chronic liver disease worldwide， and timely and precise intervention can delay disease progression and significantly reduce the risk of serious complications such as liver fibrosis， liver cirrhosis， and liver cancer. Although traditional liver biopsy combined with metabolic markers is the gold standard， it may cause complications such as pain and bleeding as an invasive examination， which has promoted scientific research to shift its focus to the construction of noninvasive assessment systems. In recent years， noninvasive diagnostic technologies based on multi-dimensional detection strategies have been continuously updated， including serological models， imaging techniques， and clinical algorithms. This article systematically reviews the screening methods for MAFLD during the fibrotic stages F1—F3， especially deep learning models based on artificial intelligence， in order to provide ideas for the early screening of MAFLD， as well as a scientific reference for optimizing disease management strategies.
- Metabolic Dysfunction-associated Fatty Liver Disease,
- Early Detection of Cance,
- Elasticity Imaging Techniques

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References(54)

References

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Early screening strategies for metabolic associated fatty liver disease

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