Natalie Burkhard

Robot-assisted surgery (RAS) using the da Vinci Surgical System has become increasingly prevalent over the last decade and holds promise for improving surgeons' accuracy and dexterity. However, the loss of direct manual contact with the surgical site results in the absence of tactile information. Surgeons instead learn to interact with tissue based mainly on visual cues. Unlike human hands, which have large sensorized surfaces, surgical graspers have small dimensions which can cause high pressures and result in crushing or damaging tissue. Surgeons aim to grasp tissue lightly enough to avoid crushing it but with sufficient grasp force to prevent accidental tissue loss. Achieving this grasp balance is challenging because the amount of force that avoids tissue damage and grasp loss simultaneously is difficult to predict, and tissue damage is not always visually apparent. Another important skill in MIS is maintaining situational awareness of all relevant anatomy and tools. Given the size and layout of many anatomies, a non-active tool may be outside of the surgeon's immediate focus or even be off-screen, making the quality of grasp difficult to monitor. 

 

Although tissue slip negatively impacts these grasping and manipulation tasks in surgery, it is a relatively unexplored topic in the literature across all surgical disciplines. My work in this domain is motivated by the idea that monitoring tissue motion between the jaws of MIS graspers has the potential to provide multiple benefits to surgeons. 

 

First, knowledge of when tissue slip will occur will enable surgeons to apply the minimum amount of grasp force to tissue and thus reduce grasper-induced tissue damage. The second anticipated benefit is that notifying surgeons of slip events in off-screen and non-active tools will provide otherwise unobservable information regarding that tool's tissue interaction and thereby reduce frustration, sudden loss of critical view, tissue tearing, etc.