Keywords: Liver, Machine Learning/Artificial Intelligence, Fat, Data Analysis.
Motivation: Accurate liver fat quantification is vital for diagnosing Metabolic-associated Steatohepatitis (MASH). Conventional IOP MRI faces limitations, and advanced CSE methods are not widely available.
Goal(s): Evaluate AI-IOP PDFF externally, compare its accuracy with 2-point Dixon SFF, and identify factors influencing PDFF estimation for model improvement.
Approach: Analyzed 526 liver MRIs with CSE-MRI and T1w-IOP sequences. Generated CSE-PDFF, Dixon SFF, and AI-IOP PDFF maps using automated segmentation and statistical analyses.
Results: AI-IOP PDFF closely matched CSE-PDFF and outperformed Dixon SFF in accuracy. External validation confirmed its reliability across settings.
Impact: AI-IOP enables reliable liver fat quantification with common T1w-IOP sequences, enhancing MASH diagnosis where advanced CSE methods are unavailable.
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