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[M007] Citation: J. Rosen, M. Solazzo, B. Hannaford, M. Sinanan, 'Task Decomposition of Minimally Invasive Surgery for Objective Evaluation of Laparoscopic Surgical Skills Using Hidden Markov Models,'
of Bioengineering, vol. 28, Supplement 1, Seattle, WA, October 2000.
Abstract
Laparoscopic skill evaluation of surgical residents is usually a subjective
process,
carried out by senior surgeons using fuzzy criteria. The aim of this study was
to develop
and assess an objective laparoscopic surgical skill scale using Hidden Markov
Models (HMM)
based on haptic information, tool/tissue interactions and visual task
decomposition.
Eight subjects (six residents: R1, R3, R5 at different training levels, and two
experts
ES)performed laparoscopic cholecystectomy on pigs using an instrumented grasper
equipped
with force-torque (F/T) sensors at the hand/tool inferace and synchronized video
of the
operative maneuvers. Fourteen types of tool/tissue interactions, each
associated with
unique F/T signatures, were defined from frame-by-frame video analysis. The
statistical distances between HMMs representing expert surgeons and residents
were
significantly different. Major differences were: (i) F/T magnitudes, (ii)
tool/tissue
interactions used and transitions between them, (iii) time intervals in each
tool/tissue
interaction and overall completion time. The greatest difference in performance
was
between R1 (junior trainee) and R3 (mid-level trainee). Smaller changes were
seen as
expertise increased beyond the R3 level. This objective learning curvey
suggests that
the laparoscopic surgical residents acquire a major portion of their skill
between the
first and the third eyars of their 5 years of training.
(formerly M143)
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Updated: Tue Jul 15 23:54:51 2008
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