Postgraduate research opportunities

The use of machine learning in diagnosis of visceral leishmaniasis

Leishmaniasis is a serious health threat. Diagnosis can be completed using different techniques but the gold standard is identifying parasites in stained samples from infected individuals. In this project we will look at using machine learning in parasite diagnosis

Number of places



13 November 2020


36 Months


2:1 Honours degree or overseas equivalent

UKRI Studentship Eligibility

The eligibility criteria for UKRI funding has changed for studentships commencing in the 2021/22 academic year. Now, all home and international students are eligible to apply for UKRI funding which will cover the full stipend and tuition fees at the home rate (not the international rate). Under the new criteria, UKRI have stipulated a maximum percentage of international students that can be recruited each year against individual training grants. This will be managed at the institutional level for all EPSRC DTP and ICASE grants. For EPSRC CDT grants, this will be managed by the individual CDT administrative/management team. For ESRC and AHRC studentships the final funding decision will be made by the respective grant holder.


To be classed as a home student, applicants must meet the following criteria:

  • Be a UK national (meeting residency requirements), or
  • Have settled status, or
  • Have pre-settled status (meeting residency requirements), or
  • Have indefinite leave to remain or enter.


The residency requirements are based on the Education (Fees and Awards) (England) Regulations 2007 and subsequent amendments. Normally to be eligible for a full award a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship (with some further constraint regarding residence for education).

If a student does not meet the criteria above, they will be classed as an international student. The international portion of the tuition fee cannot be funded by the UKRI grant and must be covered from other sources. International students are permitted to self-fund the difference between the home and international fee rates.

Project Details

Visceral leishmaniasis is a disease which causes considerable morbidity and mortality (1). Diagnosis can be carried out using molecular or immunological technique but the gold standard is identifying parasites in stained samples from infected individuals (2), which requires specialist training. However, it is now possible to train a computer to identify images using machine learning (3). In this project we will determine whether we can use machine learning to identify parasites in stained samples. We will also relate parasite burdens to specific antibody levels, using samples from Leishmania donovani infected hamsters. This project will be carried out in collaboration with Professor Revie (Department of Computing and Information Science). This project will be an excellent introduction to machine learning and how it can be used in clinical studies. The student on this project would be trained in a number of techniques including culture of parasites, use of IVIS imaging equipment, in vivo animal experiments, assessment of parasite burdens, analysis and presentation of data, and machine learning.


This is a multi-disciplinary project and would be good training for a student

Funding Details

Applicant will need to self-fund, find sponsorship for tuition and bench fees for duration of studies


Primary Supervisor: Dr K. C Carter




Secondary Supervisor: Professor C. Revie



Further information

  1. Sundar S, Singh OP, Chakravarty J. Visceral leishmaniasis elimination targets in India, strategies for preventing resurgence.
    Expert Rev Anti Infect Ther. 2018, 16:805-812.
    2. van Griensven J, Diro E. Visceral Leishmaniasis: Recent Advances in Diagnostics and Treatment Regimens. Infect Dis Clin North Am. 2019, 3:79-99.
    3. Xu J, Xue K, Zhang K. Current status and future trends of clinical diagnoses via image-based deep learning. Theranostics. 2019, 9:7556-7565.




Contact us

Primary Supervisor: Dr K. C Carter