Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning

Figure 3 Algorithmic workflow. Images were first comprehensively preprocessed to remove regions outside the ultrasound beam. A deep learning neural network, called ResNet-18, was trained on individual ultrasound images in the big data learning group. The model predicted confidences in three binary cutoffs: “≥ mild”, “≥ moderate”, or “= severe” steatosis. The confidences were mapped to a continuous image-wise score in the range of [0, 1]. View-group scores were produced by averaging each image within the group. An “All View Groups” score was produced by averaging all available view group scores. In the figure’s example, the gold standard histopathology diagnosis was a fatty cell percentage of 90%. LLL: Left liver lobe; RLL: Right liver lobe; LKC: Liver/kidney contrast; SC: Subcostal.