University of Liverpool Doctoral Network in AI for Future Digital Health

The benefits that Artificial Intelligence (AI) can bring to Health Care are extensive. Examples include: point of care diagnosis, support for medical image analysis, speeding up the drug discovery process, analysis of infectious disease spread and patient monitoring through wearable devices.

The University of Liverpool Doctoral Network in AI for Future Digital Health is directed at creating and maintaining a community of AI health care professionals that can apply the latest research within AI to Health Care. The vision is that of a world-class centre providing high-quality doctoral training within the domain of AI for Future Digital Health.

Each PhD project has been carefully co-created in collaboration with a health provider and/or a healthcare commercial partner so that the outcomes of the PhD research will be of immediate benefit. The network provides students with training, culminating in a PhD, in a collaborative environment that features, amongst other things, peer-to-peer and cohort-to-cohort based learning. On completion students will be well-placed to take up rewarding careers within the domain of AI and Digital Health.

Current Opportunities (Deadline 12 April 2020):

Application and development of machine learning approaches for automated analysis of Paediatric Brain Tumours

Rule Learning from Data – Automated Report Writing from Medical Microbiology Data

A rigorous identification of all metastable polymorphs for better and safer drugs

Towards ‘Hospices Without Walls’; How Could Human Centred Design and Robotics facilitate ‘Enhanced Independence’ and ‘Alternative Access’ in Future Palliative Care Scenarios?

Machine learning approaches for the clinical diagnosis of autoimmune disease

 

Meet our students:

Back to: Study

  • A Study of Cellular Diversity in Health and Disease Using Mass Cytometry and Computational Approaches

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    Student: Muizdeen Raji

    Supervisory team: Dr Joseph R Slupsky, Molecular and Clinical Cancer Medicine; Dr Vitaliy Kurlin, Computer Science and Materials Innovation Factory (MIF); and Dr Nagesh Kalakonda, Clatterbridge Cancer Centre

    Clustering similar cells can help biologists detect cell subsets, and their differential abundance. These cell subsets can be especially important in clinics as they can be linked to certain clinical interpretations. Thus, these cell subsets can be used for predictions. In this research a range of clustering algorithms will be experimented with to best cluster Chronic Lymphocytic Leukemia (CLL) cells obtained from CLL patients using a relatively novel single cell high dimensional technique called Mass Cytometry. The idea is to use Expectation Maximisation, an iterative method for finding maximum likelihood estimates of parameters in statistical models, and which a literature review has shown not to have been used previously in the context of cell clustering. The vision is that this research will lead to supervised learning algorithms to classify previously unseen CLL data according to the generated cluster configuration using, for example, a nearest neighbour classification approach

  • Machine Learning for The Next Generation of Paediatric Wheelchairs

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    Student: Peter Wright

    Supervisory team: Dr Paolo Paolletti, Engineering; Dr Shan Luo, Dept. Computer Science; Dr Farnaz Nickpour, Department of Civil Engineering and Industrial Design; and Mr Iain Hennessey, Clinical Director of Innovation at Alder Hey Children's Hospital

    When it comes to assistive mobility and devices, young people and children are often neglected. Most current wheelchair technology and research focuses on providing a service for disabled adults. This is for a number of reasons, such as the growth of children through their early years, and lack of data available for researchers. Whilst there is a growing sector for paediatric mobility, there are still many advances that can be made, often ergonomic considerations of the user are ignored. This project will focus on providing a new type of assistive mobility for young people. Utilising new advances in Machine Learning and Robot capabilities. The intention is to provide a platform for all young children who use wheelchairs, to have fair and equal opportunities compared to their peers. More specifically, the project aims to explore how sensing and machine learning can contribute to the creation of the next generation of paediatric powered wheelchairs.

  • The Application of Artificial Intelligence Systems to Develop Novel Diagnostic and Prognostic Tools for Hepatocellular Carcinoma

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    Student: Mohamed Elhalwagy

    Supervisory team: Prof. Philip Johnson, Dept of Molecular and Clinical Cancer Medicine; Prof. Ahmed Elsheikh, Engineering; Dr Vinzent Rolny, Roche Diagnostics GmbH, Germany; and Dr Tim Cross, Consultant hepatologist, Royal Liverpool and Broadgreen University Hospital Trust (RLBUHT)

    Hepatocellular carcinoma (HCC), "liver cancer", is the second most frequent cause of cancer-related death worldwide, and the most rapidly increasing cause of cancer mortality in the West. Current potentially curative treatments tend to have little impact, because such treatments can only be effective if the cancer is detected early enough. The current solution is focused on routine screening, however, conventional screening approaches are not ideal for large-scale surveillance. An alternative approach is to conduct blood tests for early diagnosis. Initial studies indicate that this is a viable solution. A team at Liverpool has built and validated a model for HCC diagnosis, based on a large database of UK patients that combines three serum (blood-based) biomarkers for HCC. Initial work adopted conventional biostatistical methods to model the data. However, it is conjectured a much more sophisticated model can be constructed using the methods of AI. The aim of this project is therefore to develop an effective diagnosis and screening mechanism, founded on the tools and techniques of AI, directed at blood-based tests that can be adopted routinely; thus overcoming the limitations imposed by current screening programmes.

  • An Intelligent Imaging Technology for Automatic Characterisation of the Refractive Power of the Human Eye

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    Student: Weiqiang Chen

    Supervisory team: Prof. Yaochun Shen, Electronics and Electrical Engineering (EEE); Dr Yalin Zheng, Department of Eye and Vision Science; and Prof Stephen Kaye, Royal Liverpool and Broadgreen University Hospital Trust (RLBUHT)

    Optical Coherence Tomography (OCT) is a crucial tool for the diagnosis and planning of surgery to address ocular diseases. It can achieve high resolution, high sensitivity and superior contrast compared with other technologies, such as ultrasound. In this project, a low cost, high-performance real-time 3D-OCT system will be investigated and developed to generate corneal images with sufficient depth. Since the image quality and image speed of the envisioned low-cost system may not be as good, or as fast, as a commercial OCT system, existing image segmentation algorithms based on high quality images may not work well. Thus, new AI assisted image segmentation algorithms with robust performance and new signal processing approaches will be researched and implemented to improve the system performance and this provide a comparatively low cost solution to ocular disease diagnosis and care.

  • Machine Learning of Epidemic Models

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    Student: Yanhua Xu

    Supervisory team: Dr Dominik Wojtczak, Computer Science; Prof Neil French, Institute of Infection and Global Health; and Dr Roberto Vivancos, Epidemiologist at Public Health England

    Infectious diseases kill over 17 million people a year. Reliable models of how they spread, and can be controlled, can help to reduce their impact and save lives. Typically models of the spread of infectious diseases are built manually and only, after which their parameters are adjusted to better fit the data. This is both time consuming and error-prone. However, it is also a challenging problem for general machine learning techniques due to the fact that (typically) the data available is limited. The aim of this project is to use Artificial Intelligence technology to show that providing known properties of the disease to be considered, greatly helps in learning a suitable epidemic model automatically. Two fundamental machine learning paradigms will be looked into: reinforcement learning and Hidden Markov Models. The focus fir the work is influenza, as an acute respiratory infectious disease, that effects the human respiratory system and causes many life-threatening complications such as viral pneumonia. The World Health Organization (WHO) estimates that seasonal influenza epidemics worldwide may cause 290,000 to 650,000 deaths each year. Currently, the most economical and effective measure of influenza prevention is vaccination.

  • An Intelligent Assistant for Medical Doctors when Prioritising Pathology Results

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    Student: Jing Qi

    Supervisory team: Dr Shagufta Scanlon, Computer Science; Prof. Frans Coenen, Computer Science; Dr Girvan Burnside, Biostatistics; and Mr Paul Charnley, Wirral Teaching Hospital

    In any hospital millions of computerised pathology results are produced every year. The resource required by doctors to check these results, so as to determine whether some action is required or simply to endorse the result, is substantial. The issue is compounded by additional issues such as spurious test results or uncertain risk level for potential diseases. Prioritising pathology results is thus a significant challenge. This research project is directed at the utilisation of Machine Learning techniques that can be applied to help prioritise pathology results to save doctor time and to suggest further courses of action. More specifically the work will be directed at how ordinal relationships can be learnt using machine learning, and applied in a real setting. A particular challenge is that the data available is unlabelled; the machine learning must therefore be conducted in an unsupervised manner. One idea is to priortise through the identification of outliers, another is to identify patterns and trends in existing patients that have led to changes in care and use these patterns and trends as a prioritisation mechanism.

  • Wearable sensing for non-invasive human pose estimation during sleep

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    Student: Omar Elnaggar

    Supervisory team: Dr Paolo Paolletti, Engineering; Prof. Frans Coenen, Computer Science; and Dr Andrew Hopkinson, Hopkinson Research.

    Gastrocnemius Contracture (the shortening of the muscle at the back part of the lower leg of humans) contributes to the development of many foot and ankle conditions. Prolonged immobilisation leads to muscle contractures and there is an untested clinical hypothesis that overnight inactivity can lead to Gastrocnemius Contraction if the foot is positioned in "plantar flexion" (when the foot is bent at the ankle away from the body) during sleep. The project is directed at an investigation and development of a sensor system to analyse sleep posture. It is envisioned that the sensor system will be composed of accelerometers and angle sensors which will collect the orientation of the limbs. This data will be analysed using Machine Learning tools and techniques to identify patterns and consequently understand correlations across the collected data, sleep position and the occurrence of musculoskeletal diseases such as Gastrocnemius Contracture. The measurable objective of the project is the production of a validated sleep-monitoring system and preliminary data supporting the hypothesis that there are correlations between sleep position data and musculoskeletal disease.

  • Using AI to Leverage New Forms of Data in Modelling Cycling Behaviours in the Liverpool City Region

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    Student: Aidan Watmuff

    Supervisory team: Dr. Mark Green, Geography and Planning; Dr Dannie Arribas-Bel, Geography and Planning; and Mr Huw Jenkins, Liverpool City Region Combined Authority

    Recently cycling has received much policy attention due to its ability to decrease vehicle usage and increase physical activity rates, both of which have significant health benefits. However, investment needs to be directed towards key areas to maximise impact, and this project will use Machine Learning and AI techniques to improve decision making around cycling investment. These novel methods of processing and modelling new forms of complex (big) data will provide an insight into local cycling behaviours and infrastructure provision. The PhD will develop bespoke methods that enable the benefits of using data, unexplored in the context of healthy living through cycling, to be maximised. The anticipated Machine Learning and AI methods will be designed so that they can be easily deployed within the jurisdiction of any local government to inform cycling provision. This project is designed to co-produce real-world solutions alongside the non-academic partner, the Liverpool City Region, thus maximising impact.