HMM assessment of quality of movement trajectory in laparoscopic surgery.
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.
Leong
Julian J H
JJ
Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom. j.leong@imperial.ac.uk
Nicolaou
Marios
M
Atallah
Louis
L
Mylonas
George P
GP
Darzi
Ara W
AW
Yang
Guang-Zhong
GZ
D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010535:Laparoscopy / Q000379:methods*; D008390:Markov Chains; D019060:Minimally Invasive Surgical Procedures / Q000379:methods*; D009048:Motor Skills / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011361:Professional Competence; D013647:Task Performance and Analysis