Abstract
Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data that define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We, then, perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends toward improving training efficiency by minimizing the learning curve.
Highlights
S URGICAL trainees conventionally learn and practice laparoscopic interventions on standard pelvi-trainer systems in order to improve their technical skills. Their performance is evaluated manually, using predefined scoring methods based on rating scales such as OSATS, GOALS or MISTELS [1]
Our objective is to identify all surgemes involved in the robotic surgical training task, without relying on any prior information
Surgical gesture recognition is a key component for the generation of surgical training systems, including contextaware computer-assisted systems
Summary
S URGICAL trainees conventionally learn and practice laparoscopic interventions on standard pelvi-trainer systems in order to improve their technical skills. Their performance is evaluated manually, using predefined scoring methods based on rating scales such as OSATS, GOALS or MISTELS [1]. These methods require the participation of an expert who observes and quantifies the trainee’s skills. Recent technological progress has allowed the development. Manuscript received July 1, 2015; revised September 14, 2015; accepted October 13, 2015. Date of current version October 18, 2015.
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