CAREER: Contextually Informed Autonomous Robotic Surgery

职业：上下文相关的自主机器人手术

• 批准号: 2045803
• 负责人: Michael Yip
• 金额: $ 60万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045803&HistoricalAwards=false
• 关键词: CAREER; Contextually; Informed; Autonomous; Robotic

Timely and widespread access to medical care has become one of the greatest societal concerns today, pushed to the forefront by the Covid-19 outbreak. Exacerbating the problem is that there is a growing shortage of general surgeons in the US, and hundreds of rural hospitals are projected to close in the coming years. This Faculty Early Career Development (CAREER) project produces much-needed research towards realizing autonomous surgery in the future to address these broad societal issues of healthcare access and equity challenges. Specifically, the project involves taking a big step of realizing autonomous surgical robots is imbuing them with knowledge of human anatomy and physiology in a transcribed form that can be leveraged by artificial intelligence for performing procedures autonomously. As part of the project, an educational plan that integrates closely with the research plan is planned. An interactive lesson-game for high school students is to be developed, and an experience in a classroom setting is planned in which they will explore programming and biology in a video game. To achieve the overall goal of this project, dense and reduced-order models for representing multimodal, deformable tissue environments with semantic attributes will be investigated and neural networks to approximate the forward and inverse problem. An anatomical roadmap is defined that presents the human body as a semantic navigation problem, and local context can be leveraged to perform semantic localization and environment mapping. For semantic localization, theory and strategies for leveraging semantic labels in deformable scenes and methods will be defined, utilizing geometric priors and active manipulation to identify features and classify scene objects. Finally, given contextually informed observation models, anatomical context may be used to evaluate and optimize safe plans and robust trajectories as well as higher-level decision making such as behavior trees. The educational activity involving teaching students about programming and biology will focus on teaching anatomical context from a biological perspective and basic sequential logic from a block-programming perspective. As part of an interactive lesson, the activity includes a hands-on programming portion that involves building an autonomous agent to solve an educational surgical simulator videogame. Following the interactive lesson, survey data on their perceptions regarding STEM and the potential interdisciplinary nature of biology and computer science will be collected and parsed. Then, by making the activities open-sourced and incorporating our lessons learned, we aim for our videogame to reach underrepresented minorities and provide them an opportunity to experience the broader, multi-disciplinary opportunities that computer science, engineering, and biology have to offer. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

及时和广泛地获得医疗保健已成为当今最大的社会问题之一，并被2019冠状病毒病疫情推到了最前沿。使问题更加严重的是，美国的普通外科医生越来越短缺，预计未来几年将有数百家农村医院关闭。这个教师早期职业发展（CAREER）项目产生了急需的研究，以实现未来的自主手术，以解决这些广泛的社会问题的医疗服务和公平的挑战。具体来说，该项目涉及实现自主手术机器人的一大步，即以转录形式向它们灌输人体解剖学和生理学知识，人工智能可以利用这些知识自主执行手术。作为该项目的一部分，计划制定一项与研究计划密切结合的教育计划。将为高中学生开发一个互动的教学游戏，并计划在课堂环境中进行一次体验，他们将在视频游戏中探索编程和生物学。为了实现本项目的总体目标，将研究用于表示具有语义属性的多模态、可变形组织环境的密集和降阶模型，并使用神经网络来近似正问题和逆问题。定义了一个解剖路线图，将人体作为一个语义导航问题，可以利用局部上下文来执行语义定位和环境映射。对于语义定位，将定义在可变形场景和方法中利用语义标签的理论和策略，利用几何先验和主动操纵来识别特征并对场景对象进行分类。最后，给定上下文通知的观察模型，解剖学上下文可以用于评估和优化安全计划和鲁棒轨迹以及更高级别的决策制定，例如行为树。涉及教授学生编程和生物学的教育活动将侧重于从生物学角度教授解剖学背景和从块编程角度教授基本顺序逻辑。作为互动课程的一部分，该活动包括一个动手编程部分，涉及构建一个自主代理来解决教育手术模拟器视频游戏。在互动课程之后，将收集和解析关于他们对STEM以及生物学和计算机科学潜在跨学科性质的看法的调查数据。然后，通过使活动开源并结合我们的经验教训，我们的目标是让我们的视频游戏覆盖代表性不足的少数群体，并为他们提供一个机会，体验计算机科学，工程和生物学所提供的更广泛的多学科机会。该项目由跨部门的机器人基础研究项目支持，该项目由工程部（ENG）和计算机与信息科学与工程部（CISE）共同管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Robotic Tool Tracking Under Partially Visible Kinematic Chain: A Unified Approach

• DOI: 10.1109/tro.2021.3111441
• 发表时间: 2021-02
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Florian Richter;Jingpei Lu;Ryan K. Orosco;Michael C. Yip

From Bench to Bedside: The First Live Robotic Surgery on the dVRK to Enable Remote Telesurgery with Motion Scaling

从实验室到床边：dVRK 上的首个实时机器人手术，通过运动缩放实现远程远程手术

• DOI: 10.1109/ismr48346.2021.9661536
• 发表时间: 2021
• 期刊: International Symposium of Medical Robotics
• 作者: Richter, Florian;Funk, Emily K.;Seo Park, Won;Orosco, Ryan K.;Yip, Michael C.
• 通讯作者: Yip, Michael C.