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Robotic surgery and training: Quantification of performance for evaluation and training
Abstract
The purpose of this dissertation was to quantify robotic surgical performance using objective measures. It was hypothesized that robotic surgical performance during training tasks and real robotic laparoscopic surgery can be quantified with objective performance measures by measuring the kinematics (e.g. speed) of the movements of the surgical instruments of the da Vinci Surgical System, and that the objective measures will improve with training and augmented visual feedback. Three specific aims were proposed that provide the foundation for objective evaluation of robotic surgical skill and served as a springboard for the development of a standardized training protocol for robotic surgery. Each specific aim addressed a critical issue for the understanding of surgical proficiency: (1) assessment of novice and expert performance during training tasks, (2) enhanced training with augmented visual feedback, and (3) application of performance measures to human surgical procedures. This dissertation provides a scientific basis for the evaluation of robotic laparoscopic surgical performance. The results from these studies serve as a springboard for the development of a training program in robotic laparoscopy. Specifically, this dissertation established a baseline for differentiating novice and expert performance during training tasks and correlated this performance with subjective evaluation used in resident training. Secondly, augmented visual feedback during training facilitated the development of new techniques to enhance training and improve learning. Finally, we validated performance measures during human laparoscopic procedures.
Subject Area
Biomedical research|Surgery|Robots
Recommended Citation
Judkins, Timothy N, "Robotic surgery and training: Quantification of performance for evaluation and training" (2006). ETD collection for University of Nebraska-Lincoln. AAI3237054.
https://digitalcommons.unl.edu/dissertations/AAI3237054