MP14-06 SUTURING SIMULATION VERSUS ROBOTIC PRACTICE: AN EVALUATION OF PERFORMANCE IMPROVEMENT, CONTENT, AND FACE VALIDITY IN NOVICE OPERATORS

for surgical activity recognition that is capable of identifying known activities and distinguish them from unknown activities is developed and implemented. As these activities are spatio-temporally correlated, we use a procedure-centered description in order to extract eight perceptually characteristic features that capture the three-dimensional structures of the surgical activities. RESULTS: The method was validated using 96 expert annotated samples for various surgical activities, extracted from 6 Robotassisted Radical Prostatectomies (RARP). High (512xn) dimensional feature vectors were extracted frame-wise from activity sequences in RARP. These sequences are warped non-linearly in the time domain to determine the closest sub-sequence match in the surgical procedure using Dynamic Time Warping (DTW). Similar activities tend to cluster within the same region of a multidimensional space in which the axes are the perceptual properties. Closeness between high-dimensional sequences was determined by their Manhattan distance metric (L1 norm). Our results demonstrate that actions of the same activity generate a lower score when compared to activities of a different nature (Figure). CONCLUSIONS: Automated activity sequence recognition during RAS paves the way for surgeon-independent procedures.