Early screening strategies for metabolic associated fatty liver disease
- VernacularTitle:代谢相关脂肪性肝病的早期筛查策略
- Author:
Kaiye HUA
1
;
Mengfan JIA
1
;
Yingwei ZHU
1
;
Zhonghua LU
2
;
Jian LU
3
;
Hong TANG
4
Author Information
- Publication Type:Review
- Keywords: Metabolic Dysfunction-associated Fatty Liver Disease; Early Detection of Cance; Elasticity Imaging Techniques
- From: Journal of Clinical Hepatology 2026;42(2):420-426
- CountryChina
- Language:Chinese
- 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.
