This project aims to develop smartphone imaging techniques to provide a non-invasive, accessible way of diagnosing anaemia, which has wide ranging applications from home monitoring to deployment in low resource settings. This work is part of a clinical study on anaemia in pregnant women, currently being carried out in India and Nepal.
The student will join the Department of Medical Physics and Biomedical Engineering and be part of the UCL Institute of Healthcare Engineering. The Department is proud to host internationally-leading research groups covering a broad range of activities and spread over several sites. Our staff and students have a diverse range of interests and expertise, covering many areas of physics, engineering, medicine, physiology, computer science, and mathematics. This provides a highly stimulating multidisciplinary environment for learning and for scientific research.
Primary supervisor: Dr Terence Leung (UCL Medical Physics & Biomedical Engineering)
Industrial supervisor: Dr Ihor Kirenko (Philips Research)
Clinical supervisors: Dr Sara Hillman (UCL Institute of Women’s Health), Dr Judith Meek (The Neonatal Unit, UCLH)
This project aims to capitalise on the power of smartphone imaging technology to improve the diagnostics of anaemia - one of the biggest health problems globally, affecting 1 in 4 people on earth. The current gold standard diagnosis involves a blood test, which is an invasive procedure requiring clinical expertise, specialist equipment and consumables. We are proposing a non-invasive, low-cost smartphone-based approach to detect anaemia based on promising results established in our pilot study in Ghana, in which the redness of the lower eyelids was quantified and used as a screening metric. The student will advance the current project by developing imaging techniques to segment the desired eye region automatically using AI and to minimise the effects of ambient lighting, and by incorporating the latest smartphone technology encompassing advanced camera functionalities (e.g., dual pixel sensor, macro photography and optical image stabilisation), Android machine learning (e.g. TensorFlow Lite) and image processing APIs. We will also explore its application in home monitoring for the elderly. The student will work closely with clinicians at UCL Hospitals and the Contactless Monitoring team in Philips Research, and will have the opportunity to work as an intern (3 months) in Philips Electronics’ High Tech Campus in Eindhoven.
Candidates must meet the UCL graduate entry requirements which include holding at least an upper second class degree or equivalent qualifications, in a relevant subject area such as computer science, physics, biomedical engineering or applied mathematics. A Master’s degree in a relevant discipline and additional research experience would be an advantage.
EPSRC has restrictions on eligibility, for instance related to residency within the UK, see https://epsrc.ukri.org/skills/students/help/eligibility/
Informal enquiries can be addressed to Dr Terence Leung ([email protected]
). For a formal application, please e-mail [email protected]
a covering letter outlining your motivation for the studentship and your CV. Shortlisted candidates will be required to provide transcripts of previous degrees.
Application closing date: 21 August 2019.
Expected start date: September 2019
Applicants can find out more information about another on-going project of the research team here: http://www.detect-jaundice.com