Machine Learning Models for Screening Steatosis in MASLD : An International Validation Study

作者：Junzhao Ye, Xiongcai Feng, Jiaming Lai, Xiaodong Zhuang, Xin Li, Xiaorong Gong, Hong Deng, Yiyi Hu, Zhiyong Dong, Lishu Xu, Ming‐Hua Zheng, Congxiang Shao, Bihui Zhong · 发表于：Liver International · 年份：2025 · DOI：10.1111/liv.70416 · 被引用次数：3 · 研究领域：Parathyroid Disorders and Treatments、Cardiovascular Function and Risk Factors、Bariatric Surgery and Outcomes

BACKGROUND AND AIMS: Emerging machine learning models show promise in addressing the unmet needs for the non-invasive screening of metabolic dysfunction-associated steatotic liver disease (MASLD) but lack extensive validation. We aimed to identify the most effective model for MASLD detection. METHODS: This study enrolled five cohorts: the epidemiological survey for MASLD in South China (January 2020 to March 2022), the UK Biobank database (April 2007 to December 2010), the NHANES III database (1988-1994), the NHANES 2017-2020 database and multi-centre databases with liver biopsy data from South China. The diagnosis of hepatic steatosis was established using the vibration-controlled transient elastography, magnetic resonance imaging-based proton density fat fraction, ultrasonography and biopsy. A total of 34 methods were analysed, comprising 6 machine learning models and 28 traditional scores. Survival analysis was conducted to assess the predictive value of these indicators for MASLD prognosis. RESULTS: The final analysis included a total of 24,861 subjects. The area under the receiver operating characteristic curve (AUROC) for the extreme gradient boosting (XGB) model in detecting MASLD exceeded 0.8 across all five databases. In the subgroup of lean individuals, the combination of the triglyceride-glucose index and waist circumference yielded an AUROC ranging from 0.60 to 0.88. In the NHANES databases, the overall survival rate for the MASLD group was significantly lower tha...