Collaborative Research: Smart Stent for Post-Endovascular Aneurysm Repair Surveillance

腹主动脉瘤是最常见的诊断动脉瘤，其是与血管壁逐渐变薄相关的主动脉上的异常隆起。腹主动脉瘤破裂的死亡率高达80%，在美国每年有超过10，000人死于腹主动脉瘤。最常见的治疗方法之一是血管内动脉瘤修复术，它通过微创手术在动脉瘤囊中植入覆膜支架移植物，将血流从主动脉壁重新定向，并绕过薄弱点。在接受支架的患者中，30%的患者会出现持续的血流进入动脉瘤囊，称为“内漏”，导致动脉瘤扩张和破裂。因此，应定期监测支架附近的血压和血流。然而，常用的成像技术高度依赖于患者的依从性，并且其重复施用的碘化造影剂构成慢性肾脏疾病的风险。因此，本研究的总体目标是创建一种基于柔性和无电池膜基传感器和无线生物电子学的智能支架，并采用深度学习算法实现内漏的自动诊断。这项合作研究将把科学发现和发现与跨学科学生的教育场所结合起来（电气和机械工程），代（K-12至终身学习者），以及两所学院（坦普尔大学和堪萨斯州立大学）。本研究的总体目标是开发一种用于动脉瘤腔内修复术后监测的智能支架，和无电池生物电子系统，具有深度学习算法，以实现内漏的自动诊断。中心假设是，智能支架，通过在传统支架植入物的内部和外部共形编织压电多孔膜传感器创建，将带来一种新的机电无线生物遥测方案，其传感器数据可以直接通过深度学习模型进行分析，用于复杂的血液动力学分类。所提出的研究的智力优点包括1）用于智能支架的拉胀多孔压电膜的设计，其针对血压和流量的多模态感测进行了优化，2）用于近场磁感应通信的复杂3D结构和表面集成微线圈的微制造，3）从生理信息（例如，血压和流量）转换为无线磁感应信号，4）使用精确的动脉瘤体模模型进行综合评估，以构建心血管研究的基线数据，以及5）支持深度学习的传感分类算法，可对五种不同类型的内漏进行实时、定量和自动评估。该研究将建立一个支持机器学习的无线传感系统，为下一代植入式生物医学系统激发新的理论和理解。该奖项反映了NSF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。

An abdominal aortic aneurysm is the most commonly diagnosed arterial aneurysm that is an abnormal bulge on the aorta associated with the gradual thinning of the vessel wall. With mortality as high as 80% in cases of ruptures, abdominal aortic aneurysm accounts for more than 10,000 deaths in the United States every year. One of the most common treatments is endovascular aneurysm repair, which redirects blood flow away from the aortic wall and bypasses the weak spots by implanting a covered stent graft in the aneurysm sac via a minimally invasive procedure. Among the stent recipients, 30% of them can experience persistent blood flow into the aneurysm sac, called ‘endoleak,’ leading to aneurysm expansion and rupture. Thus, blood pressure and flow near the stent should be periodically monitored. However, the commonly used imaging technique is highly dependent on patient compliance, and its repeatedly administrated iodinated contrast poses a risk of chronic kidney disease. As such, the overall objective of this research is to create a Smart Stent based on a flexible and battery-less membrane-based sensor and wireless bioelectronics with a deep-learning algorithm to realize automated diagnosis of endoleak. This collaborative research will integrate the scientific findings and discoveries with educational venues for students across disciplines (Electrical and Mechanical Engineering), generations (K-12 to lifelong learners), and two institutes (Temple University and Kansas State University).The overall objective of this research is to develop a Smart Stent for post-endovascular aneurysm repair surveillance that combines a flexible, and battery-less bioelectronic system with a deep-learning algorithm to realize automated diagnosis of endoleak. The central hypothesis is that Smart Stent, created by conformally weaving piezoelectric porous membrane sensors inside and outside of the conventional stent graft, will bring a novel electromechanical wireless biotelemetry scheme whose sensor data can be directly analyzed by a deep-learning model for classification of complex hemodynamics. The intellectual merits of the proposed research include 1) design of an auxetic porous piezoelectric membrane for the Smart Stent that is optimized for the multi-modal sensing of blood pressure and flow, 2) microfabrication of complex 3D structure and surface-integrated micro coils for near-field magnetic induction communication, 3) a novel electromechanical interrogation scheme that converts energy from physiological information (e.g., blood pressure and flow) into wireless magnetic induction signals, 4) a comprehensive evaluation using a precise aneurysm phantom model to build baseline data for cardiovascular research, and 5) a deep learning-enabled sensing classification algorithm that offers real-time, quantitative, and automated assessment of five different types of endoleak. The research will establish a machine learning-enabled wireless sensing system that will spur new theory and understanding for the next generation implantable biomedical system.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.