Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions.
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 evaluating 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.
Lin
Henry C
HC
Engineering Research Center for Computer-Integrated Surgical Systems and Technology, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA. hcl@cs.jhu.edu
Shafran
Izhak
I
Yuh
David
D
Hager
Gregory D
GD
D000465:Algorithms; D002983:Clinical Competence; D006801:Humans; D008328:Man-Machine Systems; D008962:Models, Theoretical; D009038:Motion; D011597:Psychomotor Performance; D012371:Robotics / Q000295:instrumentation* / Q000379:methods / Q000639:trends; D012984:Software; D025321:Surgery, Computer-Assisted / Q000295:instrumentation* / Q000379:methods / Q000639:trends; D013997:Time Factors; D014584:User-Computer Interface