Objective: This study evaluated the performance of automated machine-learning to diagnose non-alcoholic fatty liver disease (NAFLD) by ultrasound and compared these findings to radiologist performance. Methods: 96 patients with histologic (33) or proton density fat fraction MRI (63) diagnosis of NAFLD and 100 patients without evidence of NAFLD were retrospectively identified. The "Fatty Liver" label included 96 patients with 405 images and the "Not Fatty Liver" label included 100 patients with

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... 00 images. These 905 images made up a "Comprehensive Image" group. A "Radiology Selected Image" group was then created by selecting only images considered diagnostic by a blinded radiologist, resulting in 649 images. Cloud AutoML Visionbeta (Google LLC, Mountain View, CA) was used for machine learning. The models were evaluated against three blinded radiologists. Results : The "Comprehensive Image" group model demonstrated a sensitivity of 88.6% (73.3-96.8%) and a specificity of 95.3% (84.2-99.4%). Radiologist performance on this image group included a sensitivity of 81.0% (74.3-87.6%) and specificity of 86.0% (72.6-99.5%). The model's overall accuracy was 92.3% (84.0-97.1%), compared with mean individual performance (83.8%, 78.4-89.1%). The "Radiology Selected Image" group model demonstrated a sensitivity of 88.6% (73.3 -96.8%) and specificity of 87.9% (71.8-96.6%). Mean radiologist sensitivity was 92.4% (86.9-97.9%) and specificity was 91.9% (83.4-100%). The model's overall accuracy was 88.2% (78.1-94.8%) which was comparable to the individual radiologist performance (92.2%, 90.1-94.2%) and consensus performance (95.6%, 87.6-99.1%). Conclusions: An automated machine-learning algorithm may accurately detect NAFLD on ultrasound.