Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.666592
Title: The design and development of an intelligent atraumatic laparoscopic grasper
Author: Russell, Louise
ISNI:       0000 0004 5355 5085
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2015
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Abstract:
A key tool in laparoscopic surgery is the grasper, which is the surgeon’s main means of manipulating tissue within the body. However inappropriate use may lead to tissue damage and poor surgical outcomes. This thesis presents a novel approach to the assessment and prevention of tissue damage caused by laparoscopic graspers. The research focusses on establishing typical grasping characteristics used in surgery and thus developing a model of mechanically induced tissue trauma. A review explored the state-of-the-art in devices for measuring surgical grasping, tissue mechanics, and damage quantification to inform the research. An instrumented grasper was developed to characterise typical surgical tasks, enabling the grasping force and jaw displacement to be measured. This device was then used to quantitatively characterise grasper use in an in-vivo porcine model where the device was used to perform organ retraction and manipulation tasks. From this work, the range of forces and the grasping times used in certain tasks were determined and this information was used to guide the rest of the study. The in-vivo investigation highlighted a need for grasping in a controlled environment where the tissue’s mechanical properties could be studied. A grasper test rig was designed and developed to provide automated controlled grasping of ex-vivo tissue. This allowed the mechanical properties of tissue to be determined and analysed for indications of tissue damage. A series of experimental studies were conducted with this system which showed how the mechanical response of tissue varies depending on the applied grasping force characteristics, and how this is indicative of tissue damage through comparison to histological analysis. These data were then used to develop a model which predicts the likelihood and severity of tissue damage during grasping, based on the input conditions of grasping force and time. The model was integrated into the instrumented grasper system to provide a tool which could enable real-time grading and feedback of grasping during surgery, or be used to inform best practice in training scenarios.
Supervisor: Neville, Anne ; Culmer, Peter Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.666592  DOI: Not available
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