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Application of Data-Driven Technologies to Aid Pneumonia Diagnosis in Resource-Constrained Settings


Project Description

Background
Pneumonia is the leading cause of death among children younger than 5 year worldwide. The majority of these deaths occur in resource-constrained settings. Many pneumonia deaths can be prevented with appropriate simple interventions. Childhood pneumonia is often poorly diagnosed and untreated. Diagnosis of pneumonia relies on clinical expertise, which can be aided by advanced diagnostic tools such as X-rays and blood cultures. It is, however, not feasible to provide such health facilities in all resource-constrained settings and therefore community health workers (CHWs) need to be able to triage patients to determine whether they can be treated locally or need hospital referral. The current practice of dealing with diagnosis of pneumonia by CHWs is guided by Integrated Management of Childhood Illnesses (IMCI) provided by The World Health Organization (WHO). There are several difficulties faced when implementing IMCI in community settings worldwide. Firstly, there is over-reliance on subjective clinical assessment that to be precise requires additional training and experience. Furthermore, there is the added complexity of assessing breathing rate by manual counting which is frequently inaccurate. There is thus an urgent need to develop a more objective diagnostic tool that is low-cost and can widely be used in resource-constrained settings.

Aims
This project aims to build a diagnostic solution that relies on a pulse oximeter (a small, portable finger probe for measuring pulse rate and oxygen saturation), a digital stethoscope and a thermometer that can be integrated with a smartphone to assist clinical decision making in resource-constrained settings.

Supervisors
• Dr Syed Ahmar Shah, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh
• Professor Steve Cunningham, Centre for Inflammation Research, The University of Edinburgh

This is a fantastic opportunity for someone with an engineering/computer science background who is interested in pursuing a career in applying their engineering/computer science skills to solve pressing problems in healthcare. The University of Edinburgh is a world leader in global health and this project will provide the candidate with numerous opportunities to leverage our existing collaborations both within Edinburgh and across the world including several South Asian countries where pneumonia is still one of the leading causes of death among children. We have strong links with RESPIRE (https://www.ed.ac.uk/usher/respire), an NIHR global health research unit on respiratory health and the student will have the opportunity to develop strong collaborative links within RESPIRE for carrying out pilot studies once a suitable prototype is developed.
Requirements
In this project, we seek to appoint someone with a engineering/computer science background who is keen to apply those quantitative techniques in healthcare. A strong academic track record with a 2:1 or higher in a relevant undergraduate degree or its equivalent if outside the UK. It is also desirable to have a strong performance in a relevant postgraduate degree. Demonstrable experience in one or more of the following is desirable: signal processing, machine learning, software programming in MATLAB/C++/Python, hardware interfacing etc. The successful candidate will work in a highly interdisciplinary environment and should be able to work independently and as part of a distributed international team.

We will accept applications on a rolling basis until the position is filled. Please contact Dr Syed Ahmar Shah at for informal enquiries.

Funding Notes

This is a fully funded PhD position, which includes tuition fee (at the UK/EU rate), stipend and allowance for travel and equipment

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