MICCAI 2013 - LNCS 8149-8151

String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery

Narges Ahmidi¹, Yixin Gao¹, Benjamín Béjar², S. Swaroop Vedula¹, Sanjeev Khudanpur^{1, 3}, René Vidal^{1, 2, 3}, and Gregory D. Hager¹

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA

²Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

³Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

Abstract. The growing availability of data from robotic and laparoscopic surgery has created new opportunities to investigate the modeling and assessment of surgical technical performance and skill. However, previously published methods for modeling and assessment have not proven to scale well to large and diverse data sets. In this paper, we describe a new approach for simultaneous detection of gestures and skill that can be generalized to different surgical tasks. It consists of two parts: (1) descriptive curve coding (DCC), which transforms the surgical tool motion trajectory into a coded string using accumulated Frenet frames, and (2) common string model (CSM), a classification model using a similarity metric computed from longest common string motifs. We apply DCC-CSM method to detect surgical gestures and skill levels in two kinematic datasets (collected from the da Vinci surgical robot). DCC-CSM method classifies gestures and skill with 87.81% and 91.12% accuracy, respectively.

Keywords: surgical motion, descriptive models, gesture and skill classification, geometry, descriptive curve coding, robotic surgery

LNCS 8149, p. 26 ff.

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