In this Turing Artificial Intelligence Acceleration Fellowship, I will focus on artificial intelligence for medical treatments and therapies. I take the view that AI is a question on how to realise artificial systems that solve practical problems currently requiring human intelligence to solve, such as those solved by clinicians, nurses and therapists. Critical care is high risk and highly invasive environment caring for the sickest patients at greatest risk of death. Patients within this environment are highly monitored, enabling sudden changes in physiology to be attended to immediately. In addition, this monitoring requires a heavier staffing ratio (often 1:1 nursing; 1:8 medical) and variances in human factors and non-technical pressures (e.g. staffing, skill-mix, finances) leads to critical care delivery being disparate. AI in healthcare is a hard problem as, due to the diversity and variability of human nature, systems have to cope with unexpected circumstances when solving perceptual, reasoning or planning problems. Crucially, AI has two facets: Understanding from data, and Agency. While rapid strides have been made on learning from data, e.g. how to make medical diagnosis more precise and faster than human experts, there is little work on how to carry on after the diagnosis, e.g. which therapy and treatment to conduct. The latter requires agency and has seen fewer applications as it is a harder problem to solve. My clinical partners and I want to develop the required AI algorithms that can learn and distil the best plan of action to treat a specific patient, from the expert knowledge of clinicians. We will focus on an area of AI called RL that has been successful in enabling robots and self-driving cars to learn a form of autonomous agency. We want to transform these methods into the healthcare domain. This will require the development of new RL algorithms, able to efficiently understand the state of a patient from noisy and ambiguous hospital data. The system will not only learn to recommend interventions such as prescribing drugs and changing dosages as needed per patient but to make these recommendations in a manner that is meaningful to the clinical decision-makers and helps them make the best final decision on a course of action. The methods developed as part of this project can be used in different applications beyond healthcare. Many sectors within industry, such as aerospace, or energy, deal with similar bottlenecks. These are highly regulated environments, with great need for decisions making support, but a scarcity of highly skilled human experts. With sufficient data, our methods can be applied to these sectors as well, to distil the required human expertise and best practices from top experts, and use them to drive decision making all over the sector, for increased efficiency and safety.

Cancer is the second most common cause of death globally, accounting for 8.8 million deaths in 2015. It is estimated that radiotherapy is used in the treatment of approximately half of all cancer patients. In the UK, one new NHS proton-beam therapy facility has recently come online in Manchester and a second will soon be brought into operation in London. In addition, several new private proton-beam therapy facilities are being developed. The use of these new centres, and the research that will be carried out to enhance the efficacy of the treatments they deliver, will substantially increase demand. Worldwide interest in particle-beam therapy (PBT) is growing and a significant growth in demand in this technology is anticipated. By 2035, 26.9 million life-years in low- and middle-income countries could be saved if radiotherapy capacity could be scaled up. The investment required for this expansion will generate substantial economic gains. Radiotherapy delivered using X-ray beams or radioactive sources is an established form of treatment widely exploited to treat cancer. Modern X-ray therapy machines allow the dose to be concentrated over the tumour volume. X-ray dose falls exponentially with depth so that the location of primary tumours in relation to heart, lungs, oesophagus and spine limits dose intensity in a significant proportion of cases. The proximity of healthy organs to important primary cancer sites implies a fundamental limit on the photon-dose intensities that may be delivered. Proton and ion beams lose the bulk of their energy as they come to rest. The energy-loss distribution therefore has a pronounced 'Bragg peak' at the maximum range. Proton and ion beams overcome the fundamental limitation of X-ray therapy because, in comparison to photons, there is little (ions) or no (protons) dose deposited beyond the distal tumour edge. This saves a factor of 2-3 in integrated patient dose. In addition, as the Bragg peak occurs at the maximum range of the beam, treatment can be conformed to the tumour volume. Protons with energies between 10MeV and 250MeV can be delivered using cyclotrons which can be obtained `off the shelf' from a number of suppliers. Today, cyclotrons are most commonly used for proton-beam therapy. Such machines are not able to deliver multiple ion species over the range of energies required for treatment. Synchrotrons are the second most common type of accelerator used for proton- and ion-beam therapy and are more flexible than cyclotrons in the range of beam energy that can be delivered. However, the footprint, complexity and maintenance requirements are all larger for synchrotrons than for cyclotrons, which increases the necessary investment and the running costs. We propose to lay the technological foundations for the development of an automated, adaptive system required to deliver personalised proton- and ion-beam therapy by implementing a novel laser-driven hybrid accelerator system dedicated to the study of radiobiology. Over the two years of this programme we will: * Deliver an outline CDR for the 'Laser-hybrid Accelerator for Radiobiological Applications', LhARA; * Establish a test-bed for advanced technologies for radiobiology and clinical radiotherapy at the Clatterbridge Cancer Centre; and * Create a broad, multi-disciplinary UK coalition, working within the international Biophysics Collaboration to place the UK in pole position to contribute to, and to benefit from, this exciting new biomedical science-and-innovation initiative.

The commonest muscle disease that occurs in patients over the age of 45 years is a muscle wasting disease called inclusion body myositis (IBM). Patients typically develop progressive muscle wasting and weakness that progresses and causes marked disability and ultimately death from immobility over the course of around 10 years. There are no effective treatment for patients with IBM. The precise cause of this muscle disease is not known. However, on muscle biopsies from patients there seems to be a combination of some mild inflammation in the muscle and also an accumulation of abnormal proteins, similar to the accumulated proteins that are seen in the brains of patients with neurodegenerative diseases such as Alzheimer's, fronto-temporal dementia and motor neurone disease. Previous research has indicated that there may be genetic factors that predispose people to getting IBM but the previous studies have been quite small and not conclusive. In this research we have brought together experts in IBM from all over the world including Europe, USA and Australia to generate increased awareness of IBM, define diagnostic criteria, collect clinical information and DNA. Over the last three years we have been able to collect the largest group ever of IBM patients and DNA samples - approximately 950 cases and this number will be over 1000 once this study begins. The patient DNA and muscle tissue has been carefully stored for this work. This very large collection of DNA has put us in a very good position to undertake much more detailed genetic studies than have ever been done before to try and work out what the genetic risks factors and genes are that predispose people to this devastating disease. We plan to use the latest next generation sequencing techniques to unravel all the coding variants (those that alter proteins) that are present in 200 IBM patients DNA samples in comparison with 200 patients that are controls with normal muscles. We will analyze the DNA that we have already extracted from patients muscle tissue as this is the best diagnostic group. We will replicate the variants found in a further 700 IBM cases and over 2200 other controls. We are highly experienced in next generation sequencing technology and this has been strengthened by the recent award of a Wellcome Trust equipment grant to purchase the latest next generation sequencer. Recently we have used these techniques to identify the genetic causes of other neuromuscular disorders. In comparison with other disorders like Alzheimer's disease, where proteins are aggregated in the brain as opposed to the muscle as in IBM, the greatest advancement have been made with the identification of disease genes and genetic risk factors. If we can work out what the key genes are and how these disease causing pathways function, we will pave the way for new therapies and treatments to help patients.

Non-alcoholic fatty liver disease (NAFLD) is present in around one quarter of the population in the UK. NAFLD is associated with obesity and type 2 diabetes and arises when too much fat is deposited in the liver. This can lead to more serious conditions, including formation of scar tissue (cirrhosis) and increased risk of liver cancer, which has less than 15% survival rate over 5 years. NAFLD is an increasing problem and is expected to become the number one reason patients need a liver transplant in the next 10 years. We still don't know why fat in the liver increases the risk of cirrhosis and cancer, and treatment with drugs has not been very successful so far, despite big investment. The deposition of fat and scar tissue (fibrosis) in the liver may lead to a change in the surrounding cells' metabolic state. In addition, the liver is made of many different cell types, which can interact with each other by "cross-talk" or signalling. These cells may have different chemical compositions depending on the stage of disease. We will investigate the changes, in a range of molecules, which occur during the formation of fibrosis. We will use a special technique called mass spectrometry imaging to take molecular snapshots of healthy and scarred liver slices, in mice and humans, based on levels of biomolecules across different tissue regions and cell types. With this state-of-the-art technology, we can build a "molecular signature" across the liver to discover how normal cell metabolism is changed, leading up to the development of cirrhosis. This can help us to predict drugs that might prevent, stop or reverse the process. Cirrhosis and NAFLD are risk factors for developing liver cancer. Cancer cells in tumours are rapidly dividing and have different metabolic needs to "normal" cells. Tumours which are more aggressive or advanced may also have a different chemical make-up to those that are less aggressive/advanced. Using our mass spectrometry imaging technology, we will make a chemical fingerprint across and between tumours, to understand how the metabolism has been rewired. We will correlate findings in tissue to blood, and link metabolic signatures in tissue to molecular subtypes of HCC, based on the presence of specific mutations and/or immune cell populations. This research has the potential to uncover molecules present in patients with cancer that could be used to generate a diagnostic test in the clinic for early detection, and to decide on the best treatment based on the molecular signature. Finally, we will study the interaction of immune cells with liver cells, by combining information on the spatial distribution of metabolites with multiplex imaging of endogenous cell markers. This will require method development, including how best to carry out data fusion of the datasets. Overall our findings will reveal an unprecedented level of information on the disease mechanisms of liver cancer development from fatty liver disease and cirrhosis, which could also improve diagnostics and inform discovery of new drug treatments.

The Neuromod+ network will represent UK research, industry, clinical and patient communities, working together to address the challenge of minimally invasive treatments for brain disorders. Increasingly, people suffer from debilitating and intractable neurological conditions, including neurodegenerative diseases and mental health disorders. Neurotechnology is playing an increasingly important part in solving these problems, leading to recent bioelectronic treatments for depression and dementia. However, the invasiveness of existing approaches limits their overall impact. Neuromod+ will bring together neurotechnology stakeholders to focus on the co-creation of next generation, minimally invasive brain stimulation technologies. The network will focus on transformative research, new collaborations, and facilitating responsible innovation, partnering with bioethicists and policy makers. As broadening the accessibility of brain modification technology my lead to unintended consequences, considering the ethical and societal implications of these technological development is of the utmost importance, and thus we will build in bioethics research as a core network activity. The activities of NEUROMOD+ will have global impact, consolidating the growing role of UK neurotechnology sector.