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Title: Visual-motor learning in minimally invasive surgery
Author: White, Alan Daniel
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2016
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The purpose of this thesis was to develop an in-depth understanding of motor control in surgery. This was achieved by applying current theories of sensorimotor learning and developing a novel experimental approach. A survey of expert opinion and a review of the existing literature identified several issues related to human performance and MIS. The approach of this thesis combined existing surgical training tools with state-of-the-art technology and adapted rigorous experimental psychology techniques (grounded in the principles of sensorimotor learning) within a controlled laboratory environment. Existing technology was incorporated into surgical scenarios via the Kinematic Assessment Tool - an experimentally validated, powerful and portable system capable of providing accurate and repeatable measures of visual-motor performance. The Kinematic Assessment Tool (KAT) was first established as an appropriate means of assessing visual-motor performance, subsequently the KAT was assessed as valid when assessing MIS performance. Following this, the system was used to investigate whether the principles of ‘structural learning’ could be applied to MIS. The final experiment investigated if there is any benefit of a standardised, repeatable laparoscopic warm-up to MIS performance. These experiments demonstrated that the KAT system combined with other existing technologies, can be used to investigate visual-motor performance. The results suggested that learning the control dynamics of the surgical instruments and variability in training is beneficial when presented with novel but similar tasks. These findings are consistent with structural learning theory. This thesis should inform current thinking on MIS training and performance and the future development of simulators with more emphasis on introducing variability within tasks during training. Further investigation of the role of structural learning in MIS is required.
Supervisor: Lodge, J. P. A. ; Mon-Williams, M. ; Wilkie, R. Sponsor: Not available
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available