Leong Julian J H, Nicolaou Marios, Atallah Louis, Mylonas George P, Darzi Ara W, Yang Guang-Zhong
Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):752-9. doi: 10.1007/11866565_92.
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.
腹腔镜手术给外科医生带来了许多不同的限制，这导致先进的腹腔镜手术采用率较低。传统的手术表现评估方法依赖于对一组外科医生技术技能进行预先分类以进行验证，这可能会给结果带来主观偏差。在本研究中，隐马尔可夫模型（HMM）用于从11名能力各异的受试者中学习手术操作。通过使用留一法，在没有事先将受试者聚类到不同技能水平的情况下对HMM进行训练，输出似然性表明特定受试者的运动轨迹与该组的相似性。实验结果证明了该方法在对受试者轨迹质量进行排名方面的优势，突出了其在最小化微创手术技能评估中的主观偏差方面的价值。
