Surgical Task and Skill Classification from Eye Tracking and Tool Motion in Minimally Invasive Surgery
Identifieur interne : 000671 ( Main/Exploration ); précédent : 000670; suivant : 000672Surgical Task and Skill Classification from Eye Tracking and Tool Motion in Minimally Invasive Surgery
Auteurs : Narges Ahmidi [Canada] ; D. Hager [États-Unis] ; Lisa Ishii [États-Unis] ; Gabor Fichtinger [Canada] ; L. Gallia [États-Unis] ; Masaru Ishii [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: 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 surgeon’s 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 surgeon’s skill level with an accuracy of 82.5% and surgical task type of 77.8%.
Url:
DOI: 10.1007/978-3-642-15711-0_37
Affiliations:
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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 surgeon’s 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. 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