Quantification of hepatic steatosis on post-contrast computed tomography scans using artificial intelligence tools.

ABSTRACT:

Purpose

Early detection of steatotic liver disease (SLD) is critically important. In clinical practice, hepatic steatosis is frequently diagnosed using computed tomography (CT) performed for unrelated clinical indications. An equation for estimating magnetic resonance proton density fat fraction (MR-PDFF) using liver attenuation on non-contrast CT exists, but no equivalent equation exists for post-contrast CT. We sought to (1) determine whether an automated workflow can accurately measure liver attenuation, (2) validate previously identified optimal thresholds for liver or liver-spleen attenuation in post-contrast studies, and (3) develop a method for estimating MR-PDFF (FF) on post-contrast CT.

Methods

The fully automated TotalSegmentator 'total' machine learning model was

SUBMITTER: Derstine BA 

PROVIDER: S-EPMC12929335 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature