Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation

Journal of Medical Internet Research 2026 Open access

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Authors: Yitong Zhu, Yongshuai Wang, Shenyu Zhang, Jian Can Yang, Feng Zhang, Yan Liu, Jun Shang, Yongliang Zhang, Jizhou Wang, Lianxin Liu
DOI: 10.2196/82529
Cited by: 0
References: 49

Abstract

Abstract Background Steatotic liver disease affects 40% of nonobese individuals, but existing screening tools inadequately detect and stage disease severity in this population because of the limited sensitivity of conventional ultrasound and the lack of dedicated prediction models. Objective This study aimed to develop and validate an interpretable machine learning model specifically for multiclass hepatic steatosis severity prediction in nonobese individuals to support early risk stratification in this underrecognized group. Methods Health examination data from 215,145 nonobese participants (BMI

Fields of Study & Subjects

Liver Disease Diagnosis and Treatment

Publication details

Journal / source
Journal of Medical Internet Research
Volume
28
Pages
e82529–e82529
Publication date
15 April 2026
Language
EN