Diversity Supplement: Enabling advanced endovascular beating-heart procedures through soft robotics

多样性补充：通过软机器人技术实现先进的血管内跳动心脏手术

• 批准号: 10508008
• 负责人: Tommaso Ranzani
• 金额: $ 10.84万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10508008
• 关键词: 3D ultrasound; Anatomy; Awareness; Biological; Biology; Boston; Breathing; Cardiac; Cardiac Surgery procedures; Catheters; Charge; Cilia; Clinical; Complement; Data; Distal; Educational workshop; Elements; Engineering; Ensure; Environment; Event; Family suidae; Feedback; Female; Goals; Hair; Health; Heart; Hispanic; Image; Image-Guided Surgery; In Vitro; Industrialization; Knowledge; Length; Liquid substance; Machine Learning; Mechanics; Mentors; Mentorship; Methods; Modality; Modeling; Monitor; Myocardial Contraction; Operative Surgical Procedures; Outcome; Perception; Performance; Persons; Positioning Attribute; Procedures; Program Development; Research; Risk; Robot; Robotics; Route; Safety; Sensory; Side; Slide; Structure; Students; Surgeon; Talents; Technology; Testing; Time; Tissues; Touch sensation; Tricuspid valve structure; Underrepresented Populations; Universities; Validation; arm; base; biomaterial compatibility; cantilever; career; clinically significant; cohort; community building; design; dexterity; doctoral student; experience; experimental study; force sensor; haptic feedback; improved; innovation; machine learning algorithm; machine learning method; meetings; miniaturize; miniaturized sensor; parent grant; pressure; pressure sensor; professor; recruit; robot control; sensor; sensory input; tool; transmission process; visual feedback

Project Summary/Abstract In beating heart procedures such as tricuspid annulus reduction, only visual feedback is provided to the surgeon via imaging and the accuracy in operating surgical tools relies on the quality of the intraoperative imaging. Hence, there is a need for safe, advanced methods to monitor position and interaction forces of the robot with the heart tissues to ensure safe and accurate manipulation of delicate heart structures. This proposal aims at developing an exteroceptive probe that detects pressures applied to the heart structures by the soft robot developed in the parent grant and contact forces within the heart. Such sensing capabilities will complement conventional imaging in increasing accuracy in positioning the robot inside the beating heart. It will also allow to monitor interaction forces and contacts with the heart anatomy. Machine learning (ML) methods will be investigated to discriminate and decouple the various raw sensory inputs and use them to identify tissues the probe is in contact with for better localization, safety, and to guide the procedure. The proposed research has two aims: Aim 3 focuses on sensor design and fabrication of the probe; Aim 4 handles sensor integration with the soft deployable robot, sensory interpretation based on ML, and function validation through in-vitro and ex-vivo testing. Combined with external and internal imaging, we hypothesize that the addition of this sensor will increase the accuracy and safety during beating heart surgical procedures. To monitor pressures applied distally by the robot a soft pressure sensor will be developed. Miniaturized soft contact sensors will also be integrated on the robot. These sensors will be inspired by the hair-like cilia observed in biology. Acting similarly to cantilever beams, cilia-like extensions can deflect via fluidic drag forces producing piezoelectric charges and generating a direction-dependent potential to sense flow rate and direction. We will develop cilia-inspired sensors around the pressure sensor to monitor contacts experienced by the robot. Taking advantage of the multiple sensing modalities on the exteroceptive prove, we will investigate the use of ML application to enhance soft robot awareness and provide tissue recognition and haptic feedback to the surgeon. These additional components will improve the robot perception of the surroundings. This will lead to 1) increased accuracy in positioning by integrating the information from onboard sensing with external imaging, 2) increase safety via monitoring contact and exchanged forces with surrounding heart structures and potentially paving the way towards integration of haptic feedback, and 3) further increase the accuracy in the control of the robot by exploiting sensor data to correct model estimation errors. 1

项目摘要/摘要 在击败心脏手术（例如减少三尖瓣环）时，仅提供视觉反馈 外科医生通过成像和操作手术工具的准确性取决于术中的质量 成像。因此，需要安全，先进的方法来监视的位置和相互作用力 带有心脏组织的机器人，以确保对精致的心脏结构进行安全准确的操纵。这个建议 旨在开发一个外观感受性探针，该探针检测软机器人施加到心脏结构的压力 在父母的赠款中发展，并在心脏内部接触力量。这种感应能力将补充 传统的成像以提高将机器人定位在跳动心中的精度。它也将允许 监测与心脏解剖结构的相互作用力和接触。机器学习（ML）方法将是 调查以区分和解除各种原始感觉输入，并使用它们来识别组织 探针正在与之接触以获得更好的本地化，安全性和指导过程。 拟议的研究有两个目的：AIM 3专注于传感器设计和探针的制造；目的 4处理传感器的集成与软可部署机器人，基于ML的感觉解释以及功能 通过体外和前体内测试验证。结合外部和内部成像，我们假设 该传感器的添加将在跳动心脏手术过程中提高准确性和安全性。到 机器人远端施加的监视器将开发软压传感器。微型软接触 传感器也将集成在机器人上。这些传感器将受到观察到的类似纤毛的启发 生物学。与悬臂梁相似的作用，类似纤毛的延伸物可以通过产生的流体阻力来偏转 压电电荷并产生方向依赖的潜力，以感知流量和方向。我们将 在压力传感器周围开发纤毛启发的传感器，以监视机器人经历的接触。服用 在外部感受中，多种感应方式的优势证明，我们将研究ML的使用 用于增强软机器人意识的应用，并向外科医生提供组织识别和触觉反馈。 这些其他组件将改善机器人对周围环境的感知。这将导致1） 通过将板载感应的信息与外部成像整合到位置上提高了定位的准确性，2） 通过监测接触和与周围心脏结构交换的力来提高安全性 铺平触觉反馈整合的道路，3）进一步提高控制的准确性 通过利用传感器数据来纠正模型估计错误，机器人。 1

项目成果

A Collapsible Soft Actuator Facilitates Performance in Constrained Environments.

可折叠软执行器可提高受限环境中的性能。

• DOI: 10.1002/aisy.202200085
• 发表时间: 2022
• 期刊: Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)
• 作者: Rogatinsky,Jacob;Gomatam,Kiran;Lim,ZiHeng;Lee,Megan;Kinnicutt,Lorenzo;Duriez,Christian;Thomson,Perry;McDonald,Kevin;Ranzani,Tommaso
• 通讯作者: Ranzani,Tommaso

Magnetically induced stiffening for soft robotics.

软机器人的磁感应硬化。

• DOI: 10.1039/d2sm01390h
• 发表时间: 2023
• 期刊: Soft matter
• 影响因子: 3.4
• 作者: Gaeta,LeahT;McDonald,KevinJ;Kinnicutt,Lorenzo;Le,Megan;Wilkinson-Flicker,Sidney;Jiang,Yixiao;Atakuru,Taylan;Samur,Evren;Ranzani,Tommaso
• 通讯作者: Ranzani,Tommaso