Lab-on-an-App: AI Empowered Point-of-Care Diagnostics for Ageing Population

应用程序实验室：人工智能为老龄化人口提供护理点诊断

• 批准号: 2580492
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2580492
• 关键词: Lab; App; AI; Empowered; Point

Mobile health technology - encompassing mobile sensor, computation, communication, and user-interface capability - has the potential to support healthcare of an increasingly ageing population. It offers the opportunity to perform clinical diagnoses on the patient side, contributing to a more sustainable demand for healthcare systems in developed nations but also more widespread use of healthcare in developing ones. The overarching challenge relates to the development of high-accuracy, low-cost, portable mobile health applications capable of diagnosing a range of conditions prevalent in the older population.One such condition relates to anaemia that nowadays afflicts circa 20% of the older population. This condition is both under-recognised and under-treated; leads to additional morbidities ranging from organ damage (heart, lung), immune system disorders, or fatigue in turn contributing to falls (hence bone fractures); and contributes to the financial burden of healthcare systems. Adults with some types of anaemias were considered as a vulnerable patient group requiring shielding due to their high risk of severe SARS-CoV-2 infection during the COVID-19 pandemic.The diagnosis of anaemia requires laboratory-based measurements of a venous blood sample but this is not always readily accessible to a large fraction of the older population, preventing timely interventions. Therefore, with an increasing number of diagnoses reported each year, there is also a demand for easily accessible portable diagnosic tools.This project will develop a Lab-on-App to non-invasively diagnose anaemia and its causes (e.g. genetics, diet, or injury) that can be easily used by older people, carers, or healthcare professionals. It involves the development of:(1) Sensor technology capable of extracting information / images from the body or body fluids including (i) electrochemical sensors to measure concentration of urea on urine or (ii) multi-spectral sensors to measure skin / body fluids appearance(2) Machine learning technology that delivers diagnoses of anaemia and its causes given the data collected by the aforementioned sensors.(3) An android / ios application offering users an interface to collect data, analyse data, and deliver the diagnostics.

移动的医疗技术--包括移动的传感器、计算、通信和用户界面功能--具有支持日益老龄化的人口的医疗保健的潜力。它提供了对患者进行临床诊断的机会，有助于发达国家对医疗保健系统的更可持续需求，也有助于发展中国家更广泛地使用医疗保健。最大的挑战是开发高精度、低成本、便携式移动的健康应用程序，能够诊断老年人普遍存在的一系列疾病，其中一种疾病与贫血有关，目前约有20%的老年人患有贫血。这种疾病既未得到充分认识，也未得到充分治疗;导致其他疾病，包括器官损伤（心脏、肺）、免疫系统疾病或疲劳，进而导致福尔斯（因此骨折）;并增加医疗保健系统的经济负担。在2019冠状病毒病大流行期间，患有某些类型贫血的成年人被认为是需要防护的脆弱患者群体，因为他们感染严重SARS-CoV-2的风险很高。贫血的诊断需要对静脉血样进行实验室测量，但大部分老年人并不总是容易获得，从而无法及时干预。因此，随着每年报告的诊断数量不断增加，人们也需要易于获取的便携式诊断工具。本项目将开发一个应用程序上的实验室，以非侵入性方式诊断贫血及其原因（例如遗传学，饮食或损伤），老年人，护理人员或医疗保健专业人员可以轻松使用。它涉及以下方面的开发：（1）能够从身体或体液中提取信息/图像的传感器技术，包括（i）测量尿液中尿素浓度的电化学传感器或（ii）测量皮肤/体液外观的多光谱传感器（2）提供贫血诊断及其原因的机器学习技术，给定上述传感器收集的数据。(3)一个android / ios应用程序，为用户提供一个界面来收集数据，分析数据，并提供诊断。