TY - GEN
T1 - Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery
AU - Ahmidi, Narges
AU - Hager, Gregory D.
AU - Ishii, Lisa
AU - Fichtinger, Gabor
AU - Gallia, Gary L.
AU - Ishii, Masaru
PY - 2010
Y1 - 2010
N2 - 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%.
AB - 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%.
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U2 - 10.1007/978-3-642-15711-0_37
DO - 10.1007/978-3-642-15711-0_37
M3 - Conference contribution
C2 - 20879412
AN - SCOPUS:84857627826
SN - 3642157106
SN - 9783642157103
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 302
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Y2 - 20 September 2010 through 24 September 2010
ER -