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Despite high global prevalence of hepatic steatosis, no automated diagnosticsdemonstrated generalizability in detecting steatosis on multiple internationaldatasets. Traditionally, hepatic steatosis detection relies on cliniciansselecting the region of interest (ROI) on computed tomography (CT) to measureliver attenuation. ROI selection demands time and expertise, and therefore isnot routinely performed in populations. To automate the process, we validatedan existing artificial intelligence (AI) system for 3D liver segmentation andused it to purpose a novel method: AI-ROI, which could automatically select theROI for attenuation measurements. AI segmentation and AI-ROI method wereevaluated on 1,014 non-contrast enhanced chest CT images from eightinternational datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A,RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a meandice coefficient of 0.957. Attenuations measured by AI-ROI showed nosignificant differences (p = 0.545) and a reduction of 71% time compared toexpert measurements. The area under the curve (AUC) of the steatosisclassification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as aroutine screening method, our AI protocol could potentially allow earlynon-invasive, non-pharmacological preventative interventions for hepaticsteatosis. 1,014 expert-annotated liver segmentations of patients with hepaticsteatosis annotations can be downloaded here:https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX.

Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study