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

移动医疗技术--包括移动传感器、计算、通信和用户界面功能--有可能支持日益老龄化的人口的医疗保健。它提供了在患者端进行临床诊断的机会，有助于发达国家对医疗保健系统更可持续的需求，也有助于发展中国家更广泛地使用医疗保健。最大的挑战涉及开发高精度、低成本、便携式移动健康应用程序，能够诊断一系列在老年人中流行的疾病。其中一种疾病与贫血有关，目前约有20%的老年人患有贫血。这种情况既没有得到充分的认识，也没有得到充分的治疗；这会导致额外的疾病，从器官损伤(心脏、肺)、免疫系统紊乱或疲劳，进而导致跌倒(从而导致骨折)；并增加了医疗保健系统的经济负担。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，因为他们在新冠肺炎疫情期间感染严重SARS-CoV-2的风险很高。贫血的诊断需要基于实验室的静脉血样测量，但很大一部分老年人口并不总是容易获得这一方法，从而阻碍了及时干预。因此，随着每年报告的诊断数量的增加，对易于获得的便携式诊断工具的需求也越来越大。该项目将开发一种Lab-on-App，以非侵入性地诊断贫血及其原因(例如，遗传、饮食或损伤)，便于老年人、护理人员或医疗保健专业人员使用。它涉及的开发包括：(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)机器学习技术，根据上述传感器收集的数据提供贫血及其原因的诊断。(3)Android/iOS应用程序，为用户提供收集数据、分析数据和提供诊断的界面。

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.