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Abstract #2198

SADDLE: A Stand Alone Device for Deep Learning Execution

Justin J Baraboo1, Michael Scott1, Haben Berhane2, and Michael Markl1
1Northwestern Radiology, Chicago, IL, United States, 2Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States

The integration and evaluation of a stand-alone solution for deep learning execution within clinical environments was tested. Using a small GPU device preloaded with a data listener and pre-trained neural networks, bicuspid aortic valve patients having 4D-flow scans were pre-processed and had their aorta’s segmented automatically. Eddy current correction, noise masking, and aortic segmentation were processed sequentially. The device was integrated within the hospital’s network so that data always stayed on-site. The average time for 4D-flow processing and segmentation was roughly 10 minutes. Dice’s coefficients were 0.72±0.17, 0.87±.07, and 0.93±0.03 for eddy current correction, noise masking, and aortic segmentation.

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