Despite high global prevalence of hepatic steatosis, no automated diagnostics
demonstrated generalizability in detecting steatosis on multiple international
datasets. Traditionally, hepatic steatosis detection relies on clinicians
selecting the region of interest (ROI) on computed tomography (CT) to measure
liver attenuation. ROI selection demands time and expertise, and therefore is
not routinely performed in populations. To automate the process, we validated
an existing artificial intelligence (AI) system for 3D liver segmentation and
used it to purpose a novel method: AI-ROI, which could automatically select the
ROI for attenuation measurements. AI segmentation and AI-ROI method were
evaluated on 1,014 non-contrast enhanced chest CT images from eight
international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A,
RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean
dice coefficient of 0.957. Attenuations measured by AI-ROI showed no
significant differences (p = 0.545) and a reduction of 71% time compared to
expert measurements. The area under the curve (AUC) of the steatosis
classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a
routine screening method, our AI protocol could potentially allow early
non-invasive, non-pharmacological preventative interventions for hepatic
steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic
steatosis annotations can be downloaded here:
https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX