Lab-on-an-App: AI Empowered Point-of-Care Diagnostics for Ageing Population
Lead Research Organisation:
University College London
Department Name: Electronic and Electrical Engineering
Abstract
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.
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.
Organisations
People |
ORCID iD |
| Miguel Rodrigues (Primary Supervisor) | |
| Zhuo Zhi (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/R513143/1 | 01/10/2018 | 30/09/2023 | |||
| 2580492 | Studentship | EP/R513143/1 | 01/10/2021 | 30/09/2025 | Zhuo Zhi |
| EP/T517793/1 | 01/10/2020 | 30/09/2025 | |||
| 2580492 | Studentship | EP/T517793/1 | 01/10/2021 | 30/09/2025 | Zhuo Zhi |