First paper from Skunkworks on Machine Learning for Radiology

Left: CT of Pancreatic cyst. Right: Adam Awe, a medical student who is the lead author, presenting the results at the Shapiro forum at the UW Medical School.  He also presented at two subsequent national meetings, the Society of Abdominal Radiology and the American Roentgen Ray Society.

We are proud to report the Skunkworks has produced its first publication in the field of medicine through a collaboration between students and faculty across the Medical School, College of Engineering, and Computer Science. This work used features extracted from CT images to assess the nature of pancreatic cysts, with the long term goal of using non-invasive CT to determine the potential health risks of such cysts and assist doctors on making choices about whether to perform surgery.  A huge congratulations the team for this interdisciplinary accomplishment. We are very grateful to the visionary Machine Learning for Medical Imaging (ML4AI) program at UW for their support.

Awe, Adam M, Michael M Vanden Heuvel, Tianyuan Yuan, Victoria R Rendell, Mingren Shen, Agrima Kampani, Shanchao Liang, Dane D Morgan, Emily R Winslow, and Meghan G Lubner. 2022. “Machine Learning Principles Applied to CT Radiomics to Predict Mucinous Pancreatic Cysts.” Abdominal Radiology 47 (1): 221–31.