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HMM Assessment of Quality of Movement Trajectory in Laparoscopic Surgery

  • Julian J. H. Leong
  • Marios Nicolaou
  • Louis Atallah
  • George P. Mylonas
  • Ara W. Darzi
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

Laparoscopic surgery poses many different constraints to the operating surgeon, this has resulted in a slow uptake of advanced laparoscopic procedures. Traditional approaches to the assessment of surgical performance rely on prior classification of a cohort of surgeons’ technical skills for validation, which may introduce subjective bias to the outcome. In this study, Hidden Markov Models (HMMs) are used to learn surgical maneuvers from 11 subjects with mixed abilities. By using the leave-one-out method, the HMMs are trained without prior clustering subjects into different skills levels, and the output likelihood indicates the similarity of a particular subject’s motion trajectories to the group. The experimental results demonstrate the strength of the method in ranking the quality of trajectories of the subjects, highlighting its value in minimizing the subjective bias in skills assessment for minimally invasive surgery.

Keywords

Hide Markov Model Gaussian Mixture Model Minimally Invasive Surgery Global Rating Scale Subjective Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Julian J. H. Leong
    • 1
  • Marios Nicolaou
    • 1
  • Louis Atallah
    • 1
  • George P. Mylonas
    • 1
  • Ara W. Darzi
    • 1
  • Guang-Zhong Yang
    • 1
  1. 1.Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical TechnologyImperial College LondonLondonUnited Kingdom

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