A Cyber-Physical System for Unified Diagnosis and Treatment of Lung Disease

肺部疾病统一诊疗的网络物理系统

• 批准号: MR/Y011694/1
• 负责人: Mohsen Khadem
• 金额: $ 75.25万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY011694%2F1
• 关键词: Cyber; Physical; System; Unified; Diagnosis

This project addresses two main challenges in diagnosis and treatment of infectious lung diseases: (i) Challenges in diagnostic regional sampling of the lung in mechanically ventilated (MV) critically ill patients. Currently, sampling of lung in MV patients is not standardised. Indeed, this was exemplified before the COVID-19 pandemic by the high rate of antibiotic administration in the Intensive Care Unit (ICU) for suspected lung infections. Empirical antibiotics/therapies are often started without sampling due to the perceived risk, required expertise and time for bronchoscopic sampling. (ii) In addition to lack of repeatability and accuracy in sampling, there is a need for precise and repeatable drug delivery to guide experimental drug development. Currently, animal models of disease, which do not accurately reflect human infectious diseases, are often used for the development and iteration/optimisation of experimental drugs. It is essential to develop innovative technologies to understand and evaluate disease and drug effectiveness in humans. To this end, I will bring together roboticists, AI specialists, clinicians, and translational governance experts to develop an AI-based navigation platform that can generate a map of the lung on the fly for repeatable and accurate sampling/drug delivery. The map can be used to guide novice practitioners to perform repeatable, accurate, and timely bedside lung sampling in critically ill patients in ICUs. Thus, obviating the need for access to expert operators and democratising ICU lung sampling. Moreover, the navigation algorithm coupled with our previously developed robot for lung sampling and semi-autonomous control algorithms enables accurate drug delivery and analysis of drug-target responses to facilitate the development of effective therapies for infection-driven pathology. The project will lead to a radical innovation in respiratory critical care to democratise lung sampling/interrogation and drug delivery and enable future research for the discovery, optimisation, and development of novel therapies in severe infectious diseases.

该项目解决了传染性肺病诊断和治疗中的两个主要挑战：（i）机械通气（MV）重症患者肺部诊断区域采样的挑战。目前，MV患者的肺部采样尚未标准化。事实上，在COVID-19大流行之前，重症监护室（ICU）因疑似肺部感染而使用抗生素的比例很高。经验性抗生素/治疗通常在没有采样的情况下开始，这是由于感知的风险、所需的专业知识和支气管镜采样的时间。(ii)除了采样缺乏可重复性和准确性之外，还需要精确和可重复的药物递送来指导实验药物开发。目前，不能准确反映人类感染性疾病的动物疾病模型通常用于实验药物的开发和迭代/优化。必须开发创新技术，以了解和评估人类疾病和药物的有效性。为此，我将召集机器人专家、人工智能专家、临床医生和翻译治理专家，开发一个基于人工智能的导航平台，该平台可以实时生成肺部地图，以实现可重复和准确的采样/药物输送。该地图可用于指导新手从业者在ICU的危重患者中执行可重复、准确和及时的床旁肺采样。因此，避免了对专家操作员的需求，并使ICU肺部采样民主化。此外，导航算法与我们先前开发的用于肺部采样的机器人和半自主控制算法相结合，能够实现准确的药物输送和药物靶向反应分析，以促进感染驱动病理学的有效疗法的开发。该项目将导致呼吸重症监护的彻底创新，使肺部采样/询问和药物输送民主化，并使未来的研究能够发现，优化和开发严重传染病的新疗法。