Testing results with various training settings and inter- and intra-reader reproducibility. The full YOLOv3 network performed better than the tiny network. Although augmentation methods such as reflection and translation/scaling/contrast reduced the mis-classification rate in individual slices, these augmentation methods did not improve the performance of 3D image prescription for either the full or the tiny network. Compared to the performance of the readers (radiologists), the full network’s mis-classification rate in 2D detection was low and on par with the disagreement rate from the intra- and inter-reader reproducibility studies. The full network’s performance in 3D liver detection and axial, coronal, and sagittal prescription was comparable to that of inter-reader reproducibility. Overlap: percentage of 3D volume from manual labeling covered by AI prescription.