Laboratory for Interactive Optimization and Learning


Detecting Lymphedema


Leveraging analytics in medical diagnostics


Left: Raw 3D scan of patient

Middle: Kinect-based limb-volume scanner

Right: Volume difference left vs. right arm

joint work with

the Dixon Lab

and LymphaTech

Detecting lymphedema onset.

Nearly every breast cancer patient undergoes some type of surgical intervention, chemotherapy, or radiation as part of their therapy process, and while these procedures have proven to be very effective for cancer removal, they can create an unintended side-effect of severe, permanent arm swelling. This severe limb swelling condition is known as lymphedema and there are approximately 4 million lymphedema patients in the United States. A treatment for lymphedema exists in the form of compression garments and manual lymph massage, but the major limitation of this treatment is that there is an extremely narrow therapeutic window in which it can effectively reverse the disease. Current diagnostics cannot be implemented in a way to reliably detect lymphedema within this window. We will provide an affordable home-based diagnostic that can track limb volumes over time for early detection of lymphedema.

Develop and field test a simple, ultra-low-cost portable technology using off-the-shelf Microsoft Kinect sensors to generate digital 3D models of a patient’s geometry to extract common and novel anthropometric values as well as motion data from scans that takes less than 60 seconds to collect, are non-invasive and contact-free, and do not require skilled (medical) personnel for operation and result interpretation.