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部疾病的诊断和治疗方面的两个主要挑战：（i）机械通风（MV）重症患者肺部诊断肺部诊断区域采样的挑战。当前，MV患者中肺的采样尚未标准化。实际上，这是在涉嫌肺部感染的重症监护病房（ICU）中高的抗生素给药率在COVID-19大流行之前举例说明的。经验抗生素/疗法通常是由于感知的风险，需要的专业知识和支气管镜采样的时间而没有取样的。 （ii）除了缺乏可重复性和取样的准确性外，还需要精确且可重复的药物输送来指导实验性药物开发。当前，疾病的动物模型通常无法准确反映人类感染性疾病，通常用于实验药物的开发和迭代/优化。开发创新技术以了解和评估人类疾病和药物有效性至关重要。为此，我将汇集机器人，AI专家，临床医生和翻译治理专家，以开发一个基于AI的导航平台，该平台可以即时生成肺地图，以进行可重复且准确的抽样/药物输送。该地图可用于指导新手从业人员对重症监护病毒的重复患者进行可重复，准确和及时的卧床肺采样。因此，消除了访问专家运营商的需求并使ICU肺采样民主化。此外，导航算法加上我们先前开发的用于肺部采样和半自主控制算法的机器人，可以准确地提供药物和药物靶向反应，以促进为感染驱动的病理学开发有效的疗法。该项目将导致呼吸重症监护的根本创新，以使肺采样/审讯和药物的民主化，并为发现，优化和开发严重感染疾病的新疗法的未来研究。