“Not only can we do things faster, we can also scale the dataset up and do what was not possible before,” Garrett commented. “Using the MONAI imaging framework integrated into Flywheel, with training done on NVIDIA DGX BasePOD, we can apply our state-of-the-art research tools to every single abdominal CT we've ever performed at UW–Madison since 2004. Ten thousand cases alone used to take six to eight months just to get through, and we can now process them in a day.” This effort directly resulted in published papers on fully automated deep learning tools to improve CT-based osteoporosis assessment, CT-based liver volume segmentation, and the derivation of abdominal CT-based markers.
This has had a major impact on the timeliness of the radiologic interpretation. UW–Madison can more easily bring AI into their clinical tools and provide results instantly. For example, using AI, they can send images to the automated bone-age analyzer and receive results before the radiologist can even pick up the images.
With AI tools and frameworks from NVIDIA AI Enterprise, the university also easily replicated their workflows to other clinics and institutions. "We were starting a big clinical trial where we're deploying those tools to 21 sites around the world. In the past, we would have to ship somebody a computer or instructions on how to get this up and running. Today, we can just send them the container, get them rights to use it, and then in an hour they've already processed the first 100 cases. It has enabled us to move to a faster way to access these containers and share software."