Towards automatic skill evaluation: Detection and segmentation of robot-assisted surgical motions

Authors:
  • HLHenry Lin
    Henry Lin
  • ISIzhak Shafran
    Izhak Shafran
  • DYDavid Yuh
    David Yuh
  • GHGregory Hager
    Gregory Hager
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Abstract
This paper reports our progress in developing,techniques,for “parsing” raw motion,data from a simple surgical task into a labeled sequence,of surgical gestures. The ability to automatically,detect and segment,surgical motion,can be useful in evalua- ting surgical skill, providing surgical training feedback, or documenting essential aspects of a procedure. If processed online, the information can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. Robotic surgical systems such as the da Vinci system,from Intuitive Surgical provide a rich source of motion,and video data from surgical procedures. The application programming,interface (API) of the da Vinci outputs 192 kinematics,values at 10 Hz. Through a series of feature-processing steps, tailored to this task, the highly redundant features are projected to a compact,and discriminative,space. The resulting classifier is simple and effective. Cross-validation experiments,show,that the proposed,approach,can achieve accuracies higher than 90% when,segmenting gestures in a 4-throw suturing task, for both expert and intermediate surgeons. These preliminary results suggest that gesture-specific features can be extracted to provide highly accurate,surgical skill evaluation. Keywords: Surgical modeling, surgical skill evaluation, surgical training, robotic surgery Key link: http://www.cs.jhu.edu/CIRL/projects/SurgicalModeling

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