Surgical Task and Skill Classification from Eye Tracking and Tool Motion in Minimally Invasive Surgery
In the context of minimally invasive surgery, clinical risks are highly associated with surgeons skill in manipulating surgical tools and their knowledge of the closed anatomy. A quantitative surgical skill assessment can reduce faulty procedures and prevent some surgical risks. In this paper focusing on sinus surgery, we present two methods to identify both skill level and task type by recording motion data of surgical tools as well as the surgeons eye gaze location on the screen. We generate a total of 14 discrete Hidden Markov Models for seven surgical tasks at both expert and novice levels using a repeated k-fold evaluation method. The dataset contains 95 expert and 139 novice trials of surgery over a cadaver. The results reveal two insights: eye-gaze data contains skill related structures; and adding this info to the surgical tool motion data improves skill assessment by 13.2％ and 5.3％ for expert and novice levels, respectively. The proposed system quantifies surgeons skill level with an accuracy of 82.5％ and surgical task type of 77.8％.
Narges Ahmidi Gregory D.Hager Lisa Ishii Gabor Fichtinger Gary L.Gallia Masaru Ishii
Queens University, Kingston, ON K7L3N6, Canada Johns Hopkins University, Baltimore, MD 21211 Johns Hopkins Medical Institutions, Baltimore, MD 21287 Department of Neurosurgery, Johns Hopkins University School of Medicine,Baltimore, MD 21287
国际会议
北京
英文
295–302
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）