Two‐Step Machine Learning Model Enhances Identification of High‐Risk Metabolic Dysfunction‐Associated Steatotic Liver Disease

A machine learning-based stepwise diagnostic strategy, prioritising fibrosis assessment first, significantly improves identification of high-risk MASLD (F ≥ 2 + NAS ≥ 5). This validated approach enhances risk stratification accuracy, reduces reliance on biopsy, and offers strong clinical applicability for optimising management. Findings support integrating sequential AI diagnostics into clinical practice and future guidelines.

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Publication Details

Journal
Liver International
Published
2025-12-23
DOI
https://doi.org/10.1111/liv.70491
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Two‐Step Machine Learning Model Enhances Identification of High‐Risk Metabolic Dysfunction‐Associated Steatotic Liver Disease

F.H. Chen, Qing Ye, Kaixin Chang, Zhongjie Hu et al.
Liver International
Liver Disease Diagnosis and Treatment
article

Two‐Step Machine Learning Model Enhances Identification of High‐Risk Metabolic Dysfunction‐Associated Steatotic Liver Disease

F.H. Chen, Qing Ye, Kaixin Chang, Zhongjie Hu, Jiakun Miao, HA Fushuang, YONGFEN ZHU, Yilong Fu, Yu Xie, Minghua Zheng, Xiao Liang, Qingxian Cai, Chunyan Ye, Jiale Niu, Junping Shi, Zheng'ao Xu, Xunxun Wu, Weili Liu, Meijie Chen, Xiaoming Chen, Ruiqi Wang, Qiongyue Fan, Li Zhu
article en

Abstract

A machine learning-based stepwise diagnostic strategy, prioritising fibrosis assessment first, significantly improves identification of high-risk MASLD (F ≥ 2 + NAS ≥ 5). This validated approach enhances risk stratification accuracy, reduces reliance on biopsy, and offers strong clinical applicability for optimising management. Findings support integrating sequential AI diagnostics into clinical practice and future guidelines.

Liver InternationalVol. 46(1)
Zhejiang Chinese Medical University (CN), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Sir Run Run Shaw Hospital (CN), Southern University of Science and Technology (CN), Soochow University (CN), Tianjin Third Central Hospital (CN), Affiliated Hospital of Hangzhou Normal University (CN), Tianjin Central Hospital of Gynecology Obstetrics (CN), Zhejiang Academy of Medical Sciences (CN), Hangzhou DAC Biotech (China) (CN), Center for Special Minimally Invasive and Robotic Surgery (US), Beijing YouAn Hospital (CN), Changzhou Third People's Hospital (CN), First Affiliated Hospital of Jinan University (CN), Shenzhen Second People's Hospital (CN), Shenzhen Third People’s Hospital (CN), Tianjin Medical University (CN), Zhejiang University (CN)
National Natural Science Foundation of China, Science and Technology Department of Zhejiang Province, Science and Technology Program of Zhejiang Province
Openalex Percentile: Top 31%
Liver Disease Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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