Smart Sensing for Surgery

手术智能传感

• 批准号: EP/L014149/1
• 负责人: Guang-Zhong Yang
• 金额: $ 385.78万
• 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%2FL014149%2F1
• 关键词: Smart; Sensing; Surgery

Recent advances in surgery have made a significant impact on the management of major acute diseases, prolonging life and continuously pushing the boundaries of survival. Despite increasing sophistication of surgical intervention, complications remain common and poorly understood, contributing significantly to mortality and morbidity. Surgical site infections, catheter related sepsis, wound dehiscence and gastrointestinal anastomotic leakage are recognised complications following surgical interventions or invasive monitoring of critically ill surgical patients. Current methods for detecting these complications rely on episodic clinical examination with 'snap shot' laboratory testing. There is therefore a pressing need to develop new sensing technologies that can be seamlessly integrated with existing surgical appliances to provide continuous sensing and early detection of these adverse events, thus minimising post-operative infection, complication, and readmission. All these will also have a direct impact on healthcare economics, and more importantly the prognosis and quality-of-life of patients after surgery. The proposed project is organised into three research themes: 1) Multimodal Sensing and Miniaturised Embodiment; 2) Active Sensing with Low Power Microelectronics; and 3) Data Inferencing and Stratified Patient Management. These research themes address key technical issues related to sensor design, miniaturisation, and self-calibration, as well as low-power on-node processing, inferencing, and clinical decision support. These research themes are connected by three clinical exemplars in surgical sensing with increasing levels of technical complexity. The vision is to develop smart sensors integrated with surgical appliances and to be inserted in close proximity to the surgical site, encased within surgical drains/catheters, or placed in locations to more seamlessly monitor the systemic inflammatory response. The devices will be implanted during elective surgery or at biopsy, interrogated wirelessly, and eliminated by natural processes, or routine removal of 'hosts' such as the drains or catheters. The research programme is underpinned by extensive experience of the team in body sensor networks and bio-photonics in healthcare. Through an integrated programme of engineering research and development of a novel real-time active sensing paradigm, the project aims to transform the care pathways for surgery with greater consideration on personalised treatment, system level impact, real-time response to complications, patient concordance and quality of life. We expect that the outcome of the research will help improve surgical workflows, support safe discharge and home/community-based recovery, reduce unplanned readmissions, and influence the future of healthcare policy.

外科的最新进展对重大急性疾病的管理产生了重大影响，延长了生命，并不断突破生存的界限。尽管手术干预日益复杂，但并发症仍然很常见，而且对其了解很少，这对死亡率和发病率有很大影响。手术部位感染、导管相关性败血症、伤口裂开和胃肠吻合口漏是外科手术干预或外科危重患者侵入性监测后可识别的并发症。目前检测这些并发症的方法依赖于间歇性的临床检查和实验室测试。因此，迫切需要开发可以与现有外科器械无缝集成的新的传感技术，以提供对这些不良事件的连续传感和早期检测，从而将术后感染、并发症和再入院降至最低。所有这些都将对医疗保健经济产生直接影响，更重要的是对患者术后的预后和生活质量产生影响。拟议的项目分为三个研究主题：1)多模式传感和微型化；2)低功率微电子的有源传感；3)数据推理和分层患者管理。这些研究主题涉及与传感器设计、小型化和自我校准以及低功耗节点处理、推理和临床决策支持相关的关键技术问题。这些研究主题通过外科传感领域的三个临床范例联系在一起，技术复杂程度越来越高。我们的愿景是开发与手术器械集成的智能传感器，并将其插入手术部位附近，包裹在外科排水管/导管内，或放置在更无缝地监测全身炎症反应的位置。这些装置将在择期手术或活组织检查时植入，通过无线询问，并通过自然过程或常规的‘宿主’移除，如引流管或导管。该研究计划的基础是该团队在身体传感器网络和医疗保健领域的生物光子学方面的丰富经验。通过一个新的实时主动传感范例的工程研究和开发的综合计划，该项目旨在更多地考虑个性化治疗、系统层面的影响、对并发症的实时反应、患者的一致性和生活质量，从而改变手术的护理途径。我们预计研究结果将有助于改善手术工作流程，支持安全出院和以家庭/社区为基础的康复，减少意外再入院，并影响未来的医疗保健政策。

项目成果

Guest Editorial: MobiHealth 2014, IEEE HealthCom 2014, and IEEE BHI 2014

客座社论：MobiHealth 2014、IEEE HealthCom 2014 和 IEEE BHI 2014

• DOI: 10.1109/jbhi.2016.2559138
• 发表时间: 2016
• 期刊: IEEE Journal of Biomedical and Health Informatics
• 影响因子: 7.7
• 作者: Akay M

Objective Assessment of Arthroscopic Skills Using Novel Wireless Elbow-Worn Motion Sensors

使用新型无线肘戴式运动传感器客观评估关节镜技能

• 发表时间: 2015
• 作者: Alvand A.