Robotic surgery offers the potential to minimize surgeon's overuse injuries, promote faster, safer and lower-cost surgeries and to enable new approaches to special surgical challenges. This requires an advanced, reliable and cooperative man-machine interface that draws upon the relative strengths of each. Fundamental knowledge about how best to capture the surgeon's expertise and judgement and the machine's capabilities and shortcomings will drive these advances. Furthermore, to change the practice of medicine, these data and software must be broadly shared, and surgeons must be trained in the new surgical model. This project will develop a shared knowledge base to enable artificial intelligence (AI) to improve surgical practice. It will foster international collaborations to prepare the next generation of researchers on the conduct of robotic surgery and of international research collaborations.
The wide adoption of robotics in surgery, especially the da Vinci robot for minimally-invasive surgery, may make possible the use of AI to enhance surgical outcomes. However, no single nation can obtain enough data to represent all types of surgery or to perform the extensive testing that would be needed to validate these data. The goal of this AccelNet project is to advance research in data-driven methods to capture data on the surgical environment and surgical interventions to enable new systems that assist the surgeon or even execute tasks autonomously. This effort links multiple research networks that have already formed around shared, open research platforms for medical robotics research, exemplified by (but not restricted to) the da Vinci Research Kit (dVRK) and the Raven II surgical robot, which together are installed at more than 50 institutions worldwide. Activities for coordination and dissemination include workshops and tutorials, exchange of personnel, highly-focused surgical robotics challenges, and the development of data and software to be shared with the community.
The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts. This project was co-funded by the Dynamics, Control and Systems Diagnostics program (ENG/CMMI).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
