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What we do

What we do

What we do

Key capabilities

Our research spans a number of biomedical and quantitative disciplines. We have a particularly strong background in the following areas:

Research focussed on innovative computational and mathematical methods for analysis and modelling in healthcare. This involves the development and application of state-of-the-art mathematical/statistical tools and techniques to investigate biological processes and systems, operating at any spatial/temporal scale, or over multiple scales, from the molecular level to the whole-population level. The aim is to move towards personalised prediction in healthcare for prevention, diagnosis, or treatment across a range of diseases/conditions. Areas of expertise and capabilities include:

- Disease diagnostics and prognostics

- Theoretical and data driven modelling

- Machine learning, statistics, and data analytics

- Integrative omics and big data analysis

- Image analysis and computer vision

Research focussed on the development of next generation imaging and sensing technologies for diagnostic, monitoring and therapeutic applications; with improved accuracy, affordability and incorporating new modalities. This involves the development and translation of photonic imaging and spectroscopic technologies designed to target high impact questions in human, animal and plant health. We have a diverse research portfolio encompassing a range of clinically translational technologies, operating across length scales from single molecule detection to whole organ screening. Areas of expertise and capabilities include:

- High performance, reconfigurable microscopy suites

- Clinical translation pathways for photonic technologies

- Label-free biochemical imaging and spectroscopy

- Photonic systems development

- Real-time image and data classification

Research focussed on innovative sensing systems or analytical technologies that could have a transformative impact on prediction, diagnosis and monitoring in healthcare. This involves the development of novel diagnostic and sensor technologies which offer the potential for rapid point of care diagnosis and for the detection of new biomarkers. Areas of expertise and capabilities include:

- High resolution sensors for real-time, point of care diagnosis

- Data capture and processing in real time

- Robust, long-term sensing

- Methods for rapid, detailed analysis of chemical or biological systems

- Ultra low power sensing systems

Industrial strategy

The Government's Industrial Strategy for the UK aims to transform the UK economy, boosting productivity and earning power. Backing businesses with investment in skills, industries and infrastructure, it's five foundations are:

  • Ideas: the world’s most innovative economy
  • People: good jobs and greater earning power for all
  • Infrastructure: a major upgrade to the UK’s infrastructure
  • Business Environment: the best place to start and grow a business
  • Places: prosperous communities across the UK.

The Industrial Strategy has four 'grand challenges', which are: AI and Data, Ageing Society, Clean Growth and Future of Mobility. These provide a focus for organisations to work together and bring about transformational change. The first two of these challenges in particular align with the University of Exeter's new research and impact strategy (currently in consultation), and the Life Sciences Sector Deal. These strategies give certainty around the future use of data science in healthcare, and have helped us refine our vision for healthcare technologies and how we can help develop these:

One of the Government's grand challenge missions is to use data, artificial intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030. Our work at the Translational Research Exchange @ Exeter parallels with this mission, with researchers developing personalised healthcare to meet individuals' needs, whilst improving safety and medical diagnoses through innovation with data and technologies. 

Identifying a disease early is usually the best way to manage and minimise its effect. By better understanding the characteristics of disease, we can support healthcare professionals in diagnosing patients earlier and selecting the right treatment first time. This work is supported by a £2M award from the EPSRC which funds the Centre for Predictive Modelling in Healthcare.

Our research into developing leading edge healthcare is supported by a £1.5M award on Novel Deep Raman Spectroscopy Platform for Non-Invasive In-Vivo Diagnosis of Breast Cancer, and a £5.8M award on Raman Nanotheranostics (RaNT) - developing the targeted diagnostics and therapeutics of the future by combining light and functionalised nanoparticles.

As patients with the same disease or condition can respond differently to treatments, tailoring medicine and care for individuals is key. We research drug discovery, advanced therapies and other healthcare products, including digital technologies which speed up the time it takes to get the right treatment to the person.

Our team uses innovation to help meet the needs of an ageing society. With the aim of enabling individuals to maintain their independence, wellbeing and plan their lives with confidence, our research drives improvements in healthcare to ensure people are supported at each stage of their lives in the best possible way. We want individuals to be active for as long as possible, and for care options to have maximum impact.

Grand Challenges

The Engineering and Physical Sciences Research Council (EPSRC) has identified four Healthcare Technologies Grand Challenges which our research also aligns to. They are:

Supporting the development of novel therapies with technologies to enhance efficacy, minimise costs and reduce risk to patients.

Through this challenge we aim to support the novel engineering, ICT, mathematical and physical sciences research required to develop the drug, biological, cell and regenerative therapies of 2050. Research supported by EPSRC will seek to enhance the efficacy and precision of therapies, improve the efficiency of discovery, lower the cost of manufacturing and reduce the risk to patients from side effects.

Some specific impacts that could be achieved under this challenge include:

  • In-silico, in-vitro, and biomarker technologies for drug discovery, allowing rapid prediction and measurement of therapeutic effect, toxicology and in-vivo drug-target interaction, to reduce development costs and minimise the use of animal models.
  • Advanced drug delivery technologies to administer novel therapeutic agents effectively, targeting specific sites, allowing co-delivery of multiple agents, or providing controlled release.
  • Flexible, adaptive manufacturing processes for high-quality medicines tailored to demand, allowing cost-effective scale-up for mass production (e.g. for epidemics) and scale-down for personalisation (e.g. regenerative therapies from a patient's own cells).
  • Innovative technologies for Regenerative Medicine allowing the creation of a functional organ in the lab to repair or replace damaged organs, without the need for organ donation.
  • Advanced technologies for clinical trials, using multiscale modelling, adaptive design, and data analytics to reduce the time to market for new therapies and identify opportunities for drug-repurposing, maximising cost-benefit.

Restoring function, and optimising surgery and other physical interventions to achieve high precision with minimal invasiveness.

Through this challenge we aim to support the novel engineering, ICT, mathematical and physical sciences research required to develop prostheses and devices to restore normal function, and develop precise, minimally invasive physical interventions to repair damage or remove disease. Interventions may include established techniques such as surgery, radiotherapy or high field ultrasound, but we also encourage new approaches to physical treatment.

Some specific impacts that could be achieved under this challenge include:

  • Autonomous or cooperative robotic surgery to reduce costs and recovery times, and improve outcomes by enabling minimally invasive intervention, improving accuracy and lowering infection rates.
  • Advances in physics modelling and image guided planning for surgery and radiotherapy to improve precision/targeting, leading to fewer side-effects, faster recovery, and better outcomes.
  • New affordable, targeting methods, including but not limited to nanoscale devices, for delivering non-ionising energy into patients to revolutionise treatments for cancer and other diseases, by improving efficacy and reducing side effects.
  • Bioelectronic devices that enable long term sensing and control, which could re-establish function, reduce pain, or aid recovery.
  • Disruptive technology for implants, prostheses and assistive devices, to restore function, adapt to changing needs and capabilities, improve success-rates and longevity (e.g. reducing the need for revision surgery), and encourage uptake.

Optimising care through effective diagnosis, patient-specific prediction and evidence-based intervention.

Through this challenge we aim to support the novel engineering, ICT, mathematical and physical sciences research required to optimise treatment for the individual, improving health outcomes. Research supported by EPSRC will focus on technologies for timely and accurate diagnosis, stratification, predictive modelling, and real-time, evidence-based decision making. The aim is the right treatment at the right time.

Some specific impacts that could be achieved under this challenge include:

  • Novel, low-cost diagnostic devices, with high sensitivity, specificity and reliability, for timely and accurate diagnosis, improving the choice and reducing the cost of intervention, and increasing the likelihood of successful health outcomes.
  • Data analytic methods to identify disease phenotypes and associated responses to treatment from population data, allowing evidence-based selection of treatment options, with lower costs and morbidity, and improved health outcomes.
  • Novel non-invasive sensing platforms for the capture of real-time health and lifestyle data, enabling automated intervention - e.g. controlled release of a drug - providing better disease control and allowing patients to lead more normal, independent lives.
  • Patient-specific predictive models that integrate medical knowledge and knowledge of an individual - from medical records, imaging, physiological and behaviour monitoring, response to interventions, self-reporting etc. - for timely, accurate diagnosis and outcome prediction.
  • Systematic treatment of uncertainty in complex models and decision support systems, allowing more sophisticated decision-making, based on an understanding of confidence and sensitivity.

Using real-time information to support self-management of health and wellbeing, and to facilitate timely interventions.

Through this challenge we aim to support the novel engineering, ICT, mathematical and physical sciences research required to transform community-based health and care. Research supported by EPSRC will seek to integrate, interpret and communicate information from multiple sources, including real-time sensing, to help individuals stay healthy, and support a collaborative model of care involving patients, healthcare professionals and informal carers. This should empower individuals to self-manage effectively, and facilitate timely intervention when necessary.

Some specific impacts that could be achieved under this challenge include:

  • Methods for recognising person-specific abnormal patterns in physiological and behavioural time-course data, providing early warning of deterioration to patients, carers, and healthcare professionals.
  • Decision support dashboards and tools for healthcare professionals, supporting safe and effective management in the community of patients with long-term conditions or following early discharge.
  • An intelligent 'companion' that is fully aware of an individual's healthcare history and experience, empowering them to self-manage their health and care by providing directly relevant feedback, information and advice.
  • Individually adaptive data-collection, interaction with healthcare professionals, and self-reporting requests, to support effective care whilst minimising intrusion.
  • Technologies for promoting wellbeing by providing timely, personalised feedback, and exploiting social networking to influence health behaviours.