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[174] Citation: Abstract
Surgical robotic systems and virtual reality simulators have introduced an
unprecedented precision of measurement for both tool-tissue and tool-surgeon
interaction; thus holding promise for more objective analyses of surgical
skill. Integrative or averaged metrics such as path length, time-to-task,
success/failure percentages, etc., have often been employed towards this end
but these fail to address the processes associated with a surgical task as a
dynamic phenomena. Stochastic tools such as Markov modeling using a
'white-box' approach have proven amenable to this type of analysis. While
such an approach reveals the internal structure of the of the surgical task
as a process, it requires a task decomposition based on expert knowledge,
which may result in a relatively large/complex model. In this work, a 'black
box' approach is developed with generalized cross-procedural applications.,
the model is characterized by a compact topology, abstract state
definitions, and optimized codebook size. Data sets of isolated tasks were
extracted from the Blue DRAGON database consisting of 30 surgical subjects
stratified into six training levels. Vector quantization (VQ) was employed
on the entire database, thus synthesizing a lexicon of discrete,
task-independent surgical tool/tissue interactions. VQ has successfully
established a dictionary of 63 surgical code words and displayed
non-temporal skill discrimination. VQ allows for a more cross-procedural
analysis without relying on a thorough study of the procedure, links the
results of the black-box approach to observable phenomena, and reduces the
computational cost of the analysis by discretizing a complex, continuous
data space.
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Updated: Tue Jul 15 23:54:51 2008
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