Sensing slip of grasped biological tissue in minimally invasive surgery
- Minimally invasive surgery (MIS) is now broadly accepted as the standard of care for a number of surgical procedures, and robot-assisted surgery (RAS) has become increasingly prevalent over the last two decades. RAS in particular holds great promise for improving surgeons' accuracy and dexterity over traditional laparoscopic surgery due to enhanced vision and wristed instruments, among other factors. However, there is room for improvement: the loss of direct manual contact with the surgical site during MIS results in the absence of tactile information, so surgeons learn instead to interact with tissue based mainly on visual cues. Furthermore, unlike human hands, surgical graspers of all types (open hand-held tools, laparoscopic, RAS) have small dimensions which, if handled inappropriately, 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 not straightforward because the amount of force that avoids tissue damage and grasp loss simultaneously is difficult to predict, and tissue damage is not always immediately 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 outside the endoscope's field of view, making the quality of grasp difficult to monitor. This thesis posits that the detection of tissue slip onset can provide useful information to address these concerns and improve grasping of biological tissue during MIS and uses a RAS platform for testing. Although tissue slip negatively impacts grasping and manipulation tasks in surgery, it is a relatively unexplored topic in the literature across all surgical disciplines. Monitoring tissue motion between the jaws of MIS graspers has the potential to provide multiple benefits to surgeons. First, knowledge of when tissue slip occurs could enable surgeons to apply the minimum amount of grasp force to tissue and thus reduce overall the amount of grasper-induced tissue damage they incur. The second anticipated benefit is that notifying surgeons of slip events in off-screen and/or 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, and other negative consequences. The results of the first survey conducted on RAS surgeons' experiences with tissue slip support these ideas and are presented here. This thesis introduces a novel slip sensor to provide information regarding tissue motion onset in RAS graspers. Sensing slip of wet, compliant objects during MIS is a challenge overcome by this novel anemometric slip sensor, which provides slip onset information and direction-dependent signals. A transient thermal simulation was developed to understand the sensor's working principles and provide guidelines for its design and use. The sensor is based on hot-wire anemometry: a heater establishes a thermal gradient in the grasped tissue, and four thermal probes monitor the gradient's movement. The results from validation experiments on two sensor prototypes establish the proof of concept and demonstrate the sensing method's robustness on porcine tissue ex vivo and in vivo. Finally, a user study illustrates the sensor's potential to inform human decision making and performance during a clinically-motivated task. This thesis is an important step towards safer and more efficient MIS.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Burkhard, Natalie Tran
|Cutkosky, Mark R
|Cutkosky, Mark R
|Degree committee member
|Degree committee member
|Stanford University, Department of Mechanical Engineering.
|Statement of responsibility
|Natalie Tran Burkhard.
|Submitted to the Department of Mechanical Engineering.
|Thesis Ph.D. Stanford University 2018.
- © 2018 by Natalie Tran Burkhard
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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