CAREER: Contextually Informed Autonomous Robotic Surgery

在新冠肺炎疫情的推动下，及时和广泛获得医疗服务已成为当今最大的社会关切之一。加剧这一问题的是，美国普通外科医生越来越短缺，预计未来几年将有数百家乡村医院关闭。这个学院早期职业发展(CALEAR)项目产生了迫切需要的研究，以实现未来的自主手术，以解决这些广泛的社会问题，医疗保健获得和公平的挑战。具体地说，该项目涉及在实现自主手术机器人方面迈出一大步，向它们灌输人类解剖学和生理学知识，这种转录形式可以被人工智能用来自主执行手术。作为该项目的一部分，计划制定一项与研究计划紧密结合的教育计划。将为高中生开发一个互动的课堂游戏，并计划在课堂环境中体验，让他们在视频游戏中探索编程和生物。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通道、可变形组织环境的密集和降阶模型，并使用神经网络来逼近正问题和逆问题。定义了将人体表示为语义导航问题的解剖路线图，并且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和对场景对象进行分类。最后，在给定上下文知情的观察模型的情况下，解剖学上下文可用于评估和优化安全计划和健壮轨迹以及诸如行为树之类的更高级别的决策。教育活动包括教授学生关于编程和生物的知识，重点是从生物学的角度教授解剖学背景，从块编程的角度教授基本时序逻辑。作为互动课程的一部分，该活动包括动手编程部分，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，将收集和分析他们对STEM的看法以及生物学和计算机科学潜在的跨学科性质的调查数据。然后，通过将这些活动开源并纳入我们学到的经验教训，我们的目标是让我们的视频游戏接触到代表性不足的少数群体，并为他们提供机会体验计算机科学、工程和生物学必须提供的更广泛的、多学科的机会。该项目由跨部门机器人基础研究计划支持，该计划由工程学指导委员会(ENG)和计算机与信息科学与工程指导委员会(CEISE)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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.