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[134] Citation: J. Rosen, C. Richards, B. Hannaford, M. Sinanan,
'Hidden markov Models of Minimally Invasive Surgery,'
Studies in Health Technology and Informatics - Medicine Meets Virtual Reality, vol. 70, pp. 279-285, January, 2000.
Abstract
A crucial process in surgical education is to evaluate the level of
surgical skills. For laparoscopic surgery, skill evaluation is
traditionally performed subjectively by experts grading a video of a
procedure performed by a student. By its nature, this process is
performed using fuzzy criteria. The objective of the current study was
to develop and assess a skill scale using Discrete Hidden Markov Models
(DHMM). Ten surgeons (5 Novice Surgeons - NS; 5 Expert Surgeons)
performed a cholecystectomy and Nissen fundoplication in a porcine
model. An instrumented laparoscopic grasper equipped with a three-axis
force/torque sensor was used to measure the forces/torques at the
hand/tool interface synthronized with a video of the tool operative
maneuvers. A synthesis of frame-by-frame video analysis and a vector
quantization algorithm, defined force/torque signatures for 14 types of
tool/tissue interactions. From each stop of the surgical procedures,
two DHMM were developed representing the performance of 3 surgeons
randomly selected from the 5 in the ES and NS groups. The data obtained
by the remaining 2 surgeons in each group were used for evaluating the
performance scale. The final result was a surgical performance index
which represented a ratio of statistical similarity between the examined
surgeon's DHMM and the DHMM of NS and ES. The difference between the
performance index value, for a surgeon under study, and the NS/ES
boundary, was considered to indicate the level of expertise in the surgeon'sown group. Using the index, 87.5% of the surgical procedures werecorrectly classifi
ed into the NS and ES groups. The 12.5% of the proceduresthat were misclassified were performed by the ES and classified as NS.However, in these cases the per
formance index values were very close tothe NS/ES boundary. Preliminary data suggest that a performance indexbased on DHMM and force/torque signatures provide
s an objective means ofdistinguishing NS from ES. In addition, this methodology can be furtherapplied to evaluate haptic virtual reality surgical simulators f
orimproving realism in surgical education.
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Updated: Tue Jul 15 23:54:51 2008
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