Validation of Motion Tracking Software for Evaluation of Surgical Performance in Laparoscopic Cholecystectomy Motion tracking software for assessing laparoscopic surgical proficiency has been proven to be effective in differentiating between expert and novice performances. However, with several indices that can be generated from the software, there is no set threshold that can be used to benchmark performances. The aim of this study was to identify the best possible algorithm that can be used to benchmark expert, intermediate and novice performances for objective evaluation of psychomotor skills. 12 video recordings of various surgeons were collected in a blinded fashion. Data from our previous study of 6 experts and 23 novices was also included in the analysis to determine thresholds for performance. Video recording were analyzed both by the Kinovea 0.8.15 software and a blinded expert observer using the CAT form. Multiple algorithms were tested to accurately identify expert and novice performances. xc2xbd L  ( raisebox1ex1!left !raisebox1ex3right. ) A  ( raisebox1ex1!left !raisebox1ex6right. ) J scoring of path length, average movement and jerk index respectively resulted in identifying 2324 performances. Comparing the algorithm to CAT assessment yielded in a linear regression coefficient R2 of 0.844. The value of motion tracking software in providing objective clinical evaluation and retrospective analysis is evident. Given the prospective use of this tool the algorithm developed in this study proves to be effective in benchmarking performances for psychomotor skills evaluation.

Unsupervised Task Segmentation Approach for Bimanual Surgical Tasks using Spatiotemporal and Variance Properties In surgical workflow analysis and training in robotassisted surgery, automatic task segmentation could significantly reduce the manual labeling time and enhance robot learning efficiency. This paper presents an unsupervised segmentation approach to automatically segment a given surgical task without manual intervention. A new segmentation method is presented, which relies only on bimanual kinematic trajectories without the need for prior information about the data. Specifically, surgical tasks are segmented by fusing trajectoriesxe2x80x99 spatiotemporal and variance properties. To demonstrate the effectiveness of the proposed method, detailed experiments were first conducted on our dataset. We segmented trajectories of three different surgical stitches and observed an average F 1  score of 77.9% against the ground truths. The same trajectories were then added with different levels of noises and the segmentation comparison was made with four other methods. The proposed algorithm had demonstrated its robustness against the noises. Finally, to assess its generalization ability, the method was evaluated on publicly available JIGSAWS dataset and an average F 1  score of 75.5% was achieved.