Developing a Multimodal Prototype Device for Tissue Perfusion Monitoring

Ivan Soto, Brendan Tran Morris, Barry Claman, John Menezes

Abstract: Repaired tissue must be closely monitored for at least 48 to 72 hours after vascular anastomosis. However, this monitoring is dependent on highly trained nursing staff who are already over-extended. In this work, we develop a multimodal tissue perfusion monitoring system that utilizes machine learning for unattended alert of post operation complications. The prototype unit utilizes thermal IR, RGB, and a laser speckle images and compares their utility for classification of arterial and venous blockage. A ResNet backbone is used with early and late fusion methods for combining information from the various image modalities. Results on a new tourniquet simulation-based tissue perfusion dataset showed that late fusion using the thermal IR + RGB images performed best at classification and highlight the potential for the prototype to be used in a clinical setting.

External IDs:dblp:conf/embc/SotoMCM24