Accelerating the Radiological Workflow With AI at University of Wisconsin–Madison

Overview

The UW-Madison Department of Radiology uses NVIDIA DGX BasePOD with the MONAI imaging framework (integrated into Flywheel's healthcare data platform) and NVIDIA FLARE for federated learning to curate imaging datasets and rapidly iterate on AI models for tasks like pediatric bone age assessment and opportunistic screening. Ten thousand abdominal CT cases that previously took six to eight months to process manually can now be processed in a day; over one million images can be processed in under a day. The tools are being deployed via containers to a 21-site global clinical trial.

The challenge

UW–Madison's Department of Radiology wanted to use AI to speed up tedious tasks in radiologic interpretation, such as pediatric bone age assessment, and to enable opportunistic screening, but faced limited and imbalanced data, data drawn from disparate sources (multiple vendors, PACS, EMR, radiology dictation software), irreproducibility of studies, and subjectivity of interpretation without ground-truth analysis.

The solution

Dr. John Garrett's team uses the MONAI imaging framework integrated into Flywheel's healthcare data management platform to preprocess data, including de-identification and labeling, curating and normalizing data from multiple systems and hospitals. The university, in collaboration with other hospitals, securely trains AI models for medical imaging, annotation and classification using NVIDIA FLARE (Federated Learning Application Runtime Environment) on NVIDIA DGX BasePOD (with NVIDIA DGX A100 for training), NVIDIA Base Command, NVIDIA AI Enterprise software, and Pure Storage FlashBlade.

Reported business value

Ten thousand abdominal CT cases that previously took six to eight months to process manually can now be processed in a day, and over one million images can be processed in under a day. This directly resulted in published papers on fully automated deep learning tools for CT-based osteoporosis assessment, CT-based liver volume segmentation, and abdominal CT-based markers. Using AI, results such as automated bone-age analysis can be returned before a radiologist even picks up the images. UW-Madison is starting a clinical trial deploying these tools via containers to 21 sites around the world, where after gaining access to a container, sites have processed their first 100 cases within an hour.