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Direct haptic feedback and graphical force feedback have both been hypothesized to improve the performance of robot-assisted surgery. In this study we evaluate the benefits of haptic and graphical force feedback on surgeon performance and tissue exploration behavior during a teleoperated palpation task of artificial tissues. Seven surgeon subjects (four experienced in robot-assisted surgery) used a 7-degree-offreedom teleoperated surgical robot to identify a comparatively rigid rigid target object (representing a calcified artery) in phantom heart models using the following feedback conditions: (1) direct haptic and graphical feedback, (2) direct haptic only, (3) graphical feedback only, and (4) no feedback. To avoid the problems of force sensing in a minimally invasive surgical environment, we use a position-exchange controller with dynamics compensation for direct haptic feedback and a force estimator displayed via tool-tip tracking bar graph for graphical force feedback. Although the transparency of the system is limited with this approach, results show that direct haptic force feedback minimizes applied forces to the tissue, while coupled haptic and graphical force feedback minimizes subject task error. For experienced surgeons, haptic force feedback substantially reduced task error independent of graphical feedback.

Objectives Current robotic training approaches lack criteria for automatically assessing and tracking (over time) technical skills separately from clinical proficiency. We describe the development and validation of a novel automated and objective framework for assessment of training. Methods We are able to record all system variables (stereo instrument video, hand and instrument motion, buttons and pedal events) from the da Vinci surgical systems using a portable archival system integrated with the robotic surgical system. Data can be collected unsupervised, and the archival system does not change system operation in any way. Our open-ended multi-center protocol is collecting surgical skill benchmarking data from 24 trainees to surgical proficiency, subject only to their continued availability. Two independent experts performed structured (OSATS) assessments on longitudinal data from 8 novice and 4 expert surgeons to generate ground truth for training and to validate our computerized statistical analysis methods in identifying ranges of operational and clinical skill measures. Results Objective differences in operational and technical skill between known experts and other subjects were quantified. Longitudinal learning curves and statistical analysis for trainee performance measures are reported. Graphical representations of skills developed for feedback to the trainees are also included. Conclusions We describe an open-ended longitudinal study and automated motion recognition system capable of objectively differentiating between clinical and technical operational skills in robotic surgery. Our results demonstrate a convergence of trainee skill parameters towards those derived from expert robotic surgeons over the course of our training protocol.