Automated Support for da Vinci Surgical System

Abstract

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Mimic Technologies, Inc. Seattle, WA 98104 PERFORMING ORGANIZATION REPORT NUMBER SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for Public Release; Distribution Unlimited SUPPLEMENTARY NOTES ABSTRACTWhile adoption of da Vinci systems has been rapid worldwide, there exists a wide variance in surgical procedure performance impacting care quality, cost and patient safety negatively, due in part to inefficient training practices and limited mechanisms for objectively assessing surgical performance. To address the demand for improved training, Mimic Technologies has developed da Vinci simulators, the dV-Trainer and da Vinci Skills Simulator, in collaboration with Intuitive Surgical, which collect and analyze diverse performance data, and then recommend steps for addressing identified weaknesses. Conversely, Johns Hopkins University's Surgical Assistant Workstation collects and analyzes data from da Vinci during training and surgery. In this Phase I feasibility study, we developed a proof-of-concept working prototype framework capable of providing continuous surgical skills assessment and decision support throughout initial training and thereafter during surgery with the ultimate goal of accelerating a surgeon's acquisition of da Vinci surgical skills. Preliminary analysis of training tasks (anastomosis and peg transfer) across simulation and phantom training laboratories performed by the developed prototype framework highlights the promise of cross-platform data collection and performance measurement across training curriculum, and the opportunity for designing dynamic, customized curricula to a trainee's needs. Developed prototype framework is also a first effort towards cross-platform assessment of robotic surgical performance. This work explored several new concepts including a distributed system for decision support, the role played by instrument and hand pose in planning surgical tasks and metrics assessing this role, distinction of man-machine skills from surgical and task skills, and refinement of simulation training environments to match the corresponding real world training. Preliminary results show that selected performance metrics previously validated in simulation environment continue to hold in training exercises with a real robotic system. The...