Personalised Stent Graft Manufacturing for Endovascular Intervention

用于血管内介入治疗的个性化覆膜支架制造

• 批准号: EP/L020688/1
• 负责人: Guang-Zhong Yang
• 金额: $ 159.22万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL020688%2F1
• 关键词: Personalised; Stent; Graft; Manufacturing; Endovascular

Vascular disease is a major contributor to cardiovascular deaths in the Western world. The incidence of abdominal aortic aneurysms increases significantly with age - by over 300% for those at age 70 compared to those at age 50. With the demographic shift associated with the ageing population, there is a pressing need for early intervention and minimally invasive surgery. Endovascular therapy avoids major trauma associated with open operation, with clear advantages in terms of reduced morbidity and mortality, especially in patients unable to withstand traditional open surgery. It, however, has certain technical and anatomical constraints which are currently not addressed by the current manufacturing methods. Stent design and placement have a direct implication on the reintervention rate, which for endovascular repair is often higher than open repair. Side branches to the main artery require bespoke openings on the graft, which are expensive and time consuming to produce. The long delay in customised graft manufacturing can subject patients to the risk of aneurysm rupture and precludes treatment to patients with acute symptoms. Improved manufacturing of personalised stent-grafts is therefore an unmet clinical demand that requires innovative manufacturing solutions. This project aims to develop a novel robotic platform for personalised stent-graft manufacturing. It includes the development of a new stent graft sewing robot combined with a fully personalised aortic mould printed with the latest 3D printing technologies using patient-specific imaging data. It improves consistency, speed and quality of customised manufacturing of stent graft with complex geometries and represents an ideal example of the innovative use of robotics, additive manufacturing, and machine vision for small run, 'batch'-of-one personalised medical devices. The project is to be carried out by a multidisciplinary team with complementary research skills in robotics, sensing, imaging, and endovascular intervention, alongside two major industrial partners in medical devices and surgical robotics.

在西方世界，血管疾病是心血管疾病死亡的主要原因。腹主动脉瘤的发病率随着年龄的增长而显著增加--70岁的人与50岁的人相比增加了300%以上。随着人口老龄化带来的人口结构变化，迫切需要早期干预和微创手术。血管内治疗避免了与开放手术相关的重大创伤，在降低发病率和死亡率方面具有明显的优势，特别是在无法忍受传统开放手术的患者。然而，它具有某些技术和解剖限制，这些限制目前没有被当前的制造方法解决。支架的设计和放置直接影响再干预率，血管内修复的再干预率往往高于开放修复。主动脉的侧支需要在移植物上定制开口，这是昂贵和耗时的。定制移植物制造的长期拖延可能会使患者面临动脉瘤破裂的风险，并使有急性症状的患者无法接受治疗。因此，个性化支架移植物的改进制造是一个尚未满足的临床需求，需要创新的制造解决方案。该项目旨在开发一种用于个性化支架移植制造的新型机器人平台。它包括开发一种新的支架移植物缝合机器人，结合使用患者特定成像数据的最新3D打印技术打印的完全个性化的主动脉模具。它提高了具有复杂几何形状的支架移植物定制制造的一致性、速度和质量，是创新使用机器人技术、加法制造和机器视觉技术用于小批量、一次性个性化医疗设备的理想范例。该项目将由一个在机器人、传感、成像和血管内干预方面拥有互补研究技能的多学科团队与医疗器械和手术机器人领域的两个主要工业合作伙伴共同实施。

项目成果

Task-Based LSTM Kinematic Modeling for a Tendon-Driven Flexible Surgical Robot

• DOI: 10.1109/tmrb.2021.3127366
• 发表时间: 2022-05
• 期刊: IEEE Transactions on Medical Robotics and Bionics
• 作者: Weibang Bai;Francesco Cursi;Xiaotong Guo;Baoru Huang;Benny P. L. Lo;Guang-Zhong Yang;E. Yeatman

Tissue Oxygenation Sensor and an Active In Vitro Phantom for Sensor Validation

组织氧合传感器和用于传感器验证的主动体外模型

• DOI: 10.1109/jsen.2019.2917122
• 发表时间: 2019
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Chen C

Design and Fabrication of a 3-D Printed Metallic Flexible Joint for Snake-Like Surgical Robot

• DOI: 10.1109/lra.2019.2896475
• 发表时间: 2019-04-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Hu, Yang;Zhang, Lin;Yang, Guang-Zhong
• 通讯作者: Yang, Guang-Zhong

Visual Guidance and Automatic Control for Robotic Personalized Stent Graft Manufacturing

机器人个性化覆膜支架制造的视觉引导和自动控制

• DOI: 10.1109/icra.2019.8794123
• 发表时间: 2019
• 作者: Guo Y

Cross-Scene Suture Thread Parsing for Robot Assisted Anastomosis based on Joint Feature Learning

• DOI: 10.1109/iros.2018.8593622
• 发表时间: 2018-10
• 期刊: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Yun Gu;Yang Hu;Lin Zhang;J. Yang;Guang-Zhong Yang