Collaborative Research: AccelNet: International Collaboration to Accelerate Research in Robotic Surgery

机器人手术可以最大限度地减少外科医生的过度使用伤害，促进更快、更安全、更低成本的手术，并为特殊手术挑战提供新方法。这需要一个先进的、可靠的和协作的人机界面，利用每个人的相对优势。关于如何最好地捕捉外科医生的专业知识和判断以及机器的能力和缺点的基本知识将推动这些进步。此外，为了改变医学实践，这些数据和软件必须广泛共享，外科医生必须接受新的手术模式的培训。该项目将开发一个共享知识库，使人工智能（AI）能够改善外科实践。它将促进国际合作，为机器人手术和国际研究合作的下一代研究人员做好准备。机器人技术在外科手术中的广泛应用，特别是用于微创手术的达芬奇机器人，可能使人工智能的应用成为可能，以提高手术效果。然而，没有一个国家能够获得足够的数据来代表所有类型的手术，或者进行广泛的测试来验证这些数据。AccelNet项目的目标是推进数据驱动方法的研究，以捕获手术环境和手术干预的数据，从而使新系统能够协助外科医生甚至自主执行任务。这项工作将多个研究网络连接起来，这些研究网络已经围绕医疗机器人研究的共享、开放研究平台形成，例如（但不限于）达芬奇研究工具包（dVRK）和Raven II手术机器人，它们一起安装在全球50多家机构中。协调和传播的活动包括研讨会和教程、人员交流、高度集中的外科机器人挑战，以及与社区共享的数据和软件的开发。通过国际网络对网络合作加速研究（AccelNet）计划旨在加速科学发现的进程，并为下一代美国研究人员进行多团队国际合作做好准备。AccelNet项目支持美国研究网络与国外互补网络之间的战略联系，将利用研究和教育资源来应对需要重大国际协调努力的重大科学挑战。该项目由动力学、控制和系统诊断项目（ENG/CMMI）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.