Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes
A machine learning–driven web tool based on 13 standard patient metrics demonstrates strong predictive performance for MASLD, supporting early clinical intervention.
An interpretable machine learning (ML) model can accurately predict metabolic dysfunction–associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus (T2DM), a population at particularly high risk for liver complications.
MASLD is highly prevalent among adults with T2DM, affecting approximately 65%, and carries a substantially greater risk for progression to more severe liver diseases, including cirrhosis and hepatocellular carcinoma, compared with MASLD alone.
Published in Diabetes, Obesity and Metabolism, the analysis addresses the limitations of current non-invasive diagnostic tools, which often perform suboptimally in this high-risk population.
Author summary: Machine learning improves MASLD detection.
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