Bowel cancer causes nearly a million deaths per annum, and more than half of cases are fatal. Although early cancer detection can significantly improve patients' outcomes, more than half of bowel cancer cases in the UK are diagnosed at a late stage. Detection of bowel cancer and pre-cancerous polyps is currently predominantly performed by either visual inspection of the colonic mucosa during endoscopy (colonoscopy), which is an invasive procedure, or by cross-sectional imaging, which is less reliable for small-sized lesions that are not easily visualised. If such polyps are not detected and removed at an early stage, there is a chance that they may become cancerous. Recently, direct visualisation of the colon using colonic capsule endoscopy has been introduced, but uptake by clinicians has been limited due to concerns about missed lesions with this modality, with potentially catastrophic outcomes for patients. Leveraging our pioneering work in the field of controllable capsule technologies, our team has developed a novel, untethered, vibrational, AI-assisted, ingestible pill sensor, with the aim of detecting small colonic lesions by a new modality (DOI:10.1109/LRA.2023.3251853). After swallowing this pill-based device, it will pass through the patient's gastrointestinal (GI) tract through gut motility and peristalsis. Dynamic signals from the pill in contact with in-situ bowel lesions can be acquired and analysed for features that are indicative of biomechanical changes in the tissues, to infer benign or malignant lesions. Design innovations include the vibrational mechanism for encouraging pill-lesion interactions, a portable platform for interaction signal acquisition, and disposable components: after each procedure, the pill's outer shell is discarded, whilst the main components are reclaimed without reprocessing, dramatically reducing the cost. The platform is small and lightweight, with significantly reduced capital costs compared to standard colonoscopy. Early experiments in our laboratory demonstrate an average accuracy of 96.5% in successful detection of simulated colonic lesions (DOI:10.1109/LRA.2023.3251853). This novel pill sensor is therefore planned to facilitate an alternative to both colonoscopy and current colonic capsule endoscopy. This project aims to develop an initial prototype of this pill sensor in fusion with artificial intelligence to aid the detection of hard-to-visualise bowel lesions. This novel diagnostic tool will be developed, optimised, and tested in this 18-month research programme. By the end of this project, we will deliver a proof-of-concept prototype at TRL 3 that is ready for in-vivo testing. To this end, we will pursue this aim by (1) integrating all the required components, including on-board vibrator and sensor (e.g., accelerometer), data storage, and power supply, (2) optimising the controllability and integrity of the pill, (3) using the dynamic signals acquired by the on-board sensor to perform autonomous detection of lesions, (4) benchmarking the proposed technology with standard colonic capsule endoscopy in multiple validation scenarios in a laboratory setting, (5) optimising the initial prototype for clinical use based on clinical feedback. In the long term, this work will initiate a new, minimally invasive, investigative modality for patients and clinicians that is comfortable, safe, reliable, accurate and cost-effective in the detection of pre-cancerous and early cancerous lesions.

With an increasingly ageing population, neurological disorders (ND), including Alzheimer's and Parkinson's disease (AD and PD), are becoming the second leading cause of death and the world's largest cause of disability-adjusted life years. Currently, incurable ND have a devastating impact on individuals, families and a heavy economic burden on societies. Early diagnosis and longitudinal monitoring of ND, such as for AD, is extremely important for their treatment, care and on-going research. However, current ND diagnosis approaches, such as cognitive and physical assessment, invasive tests (obtaining biological samples), or neuroimaging scans (e.g. positron emission tomography, magnetic resonance imaging), are often either very subjective and uncomfortable, or very capital intensive and time-consuming. In this project, we propose a new computational framework that integrates novel nonlinear systems engineering and network analysis for the diagnosis and characterisation of ND based on electroencephalography (EEG) recordings. EEG measures brain electrical activity through small electrodes attached to the scalp (with each electrode called an EEG channel). EEG has the advantage of a relatively low cost (i.e. £100's-£10,000's compared to millions of pounds for magnetic resonance imaging), better accessibility and portability, user-friendliness and, importantly, superior temporal resolution (i.e. high sampling rate with millisecond precision). Current EEG approaches predominantly employ either the analysis of a single EEG channel or the analysis of pairs of channels using simple (linear) methods that cannot capture the full complexity of the information, and focus on a selected local brain region. The novelty of our new approach will be to characterise ND by analysing the brain as a network using non-linear (cross-frequency) methods. Emerging evidence suggests that cross-frequency coupling (CFC), between different frequency bands, is the key mechanism in the integration of (local and global) communication in the brain across spatial-temporal scales, and thus this project seeks to investigate its role in the development and progression of ND. Our goal will be realised through the deliverables from four technical work packages (WPs), namely: (1) development (for the first time) of a unified framework to identify and quantify CFC from a systems engineering approach (i.e. nonlinear system identification); (2) development of a novel multi-layer cross-frequency network approach and extraction of global network features; (3) identification of important brain regions for nonlinear dynamic analysis, and; (4) the integration of both local nonlinear CFC features and global network features for diagnostic purposes. Compared with current machine/deep learning techniques (e.g. recurrent or graph neural networks), our proposed novel approach will provide human interpretable results in addition to the standard classification performance metrics. It will uncover whether linear or nonlinear interactions, the type and variation of nonlinear interactions (e.g. CFC, energy transfer) and which brain regions (EEG channels), are involved in neurodegeneration. Such information can be crucial for developing an interpretable, accurate diagnosis and, eventually, the management of ND. For example, knowing the specific CFC and brain regions involved will not only facilitate the diagnosis of PD, but may also help improve the treatment (i.e. deep brain stimulation) through a more accurate stimulation at specific frequency ranges and brain regions. We will develop the methodology and evaluate the feasibility of our approach based on the analysis of (anonymised) EEG data collected from AD and PD patients and healthy controls, through the close collaboration and guidance from our project partners, including clinical neurologists at NHS Royal Devon and Exeter Hospital and the University of Sheffield.

Information processing is shared among seemingly different nonlinear complex dynamics. In particular, there has been a growing interest in information geometry that refers to the application of differential geometry to probability and statistics by defining the notion of metric (distance) in statistical manifolds. In particular, it provides us with a powerful method for understanding random stochastic processes for theoretical and practical purposes. Conceptually, assigning a metric to probability density functions (PDFs) enables us to quantify the difference among different PDFs and thus to make a beautiful link between a stochastic process, complexity, and geometry. This project aims to develop a new model-free information geometric theory of neural information processing for the practical purpose of improved disorder diagnosis by overcoming various current challenges described below. Brains are complex information-processing organs whose proper function in different parts is indispensable for our optimal well-being. Many critical issues, such as understanding neural information processing and diagnosis of neurological disorders, require the identification of not only regional activation, but also the causal connectivity among different regions of the brain and the simplest possible circuit to explain observed responses. In particular, causality (effective connectivity) analysis offers new diagnostic opportunities for a whole range of neurological disorders. Therefore, the study of classical structural connectivity is complemented by functional connectivity and, crucially, causal analysis through statistical modelling of neurophysiological signals (e.g., such as functional magnetic resonance imaging and electroencephalography (EEG)). The main challenges in neurological signal analysis stem from the uncertain time-varying nonlinear dynamics of the human brain. Data are generally non-stationary and non-Gaussian, while the mean value, variance, or other higher moments can abruptly change in time. Such data cannot be adequately quantified in the traditional formulation of transfer entropy and Granger causality based on stationary or Gaussian data. Furthermore, underlying mathematical models are not always available to fit the data. On the other hand, the reduced signal-to-noise ratio in data often hampers an accurate analysis. It is thus critical to develop a model-free method that can effectively quantify dynamic changes in data. To face these challenges, we will take our leading-edge research on information geometry as our starting point and develop the method further to quantify non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity most effectively. To this end, we propose a one-year, synergistic program on theoretical and computational studies and data analysis by harnessing the complementary skills of our team. Specifically, we will: i) extend our theory to nonlinear/multiple variables; ii) numerically simulate simple neural activity models. In parallel, we will: iii) apply our new methods to analyse the anonymised EEG data from healthy control groups and patients with certain neurological disorders (e.g., epilepsy, Parkinson's disease with normal cognitive function, Alzheimer's disease). In particular, we will compare information processing and brain connectivities among key regions of the brain in healthy control groups and patients and identify their similarities and differences. We will then develop biomarkers to diagnose neurological disorders, such as seizures, and track disease progression while exploring clinical implications. This project will be a stepping stone for future proposals to address other practical challenges given the increasingly important role of information theory across disciplines.

Gastrointestinal (GI) disease is the third most common cause of death, the leading cause of cancer death, and the most common cause of hospital admission. The burden of GI disease in the UK is heavy for patients, the National Health Service (NHS), and the economy. Endoscopy plays a vital role in the diagnosis of GI disorders, and the demand for GI endoscopy has doubled in the past 5 years, with on-going growth of 6.5% per annum predicted by the NHS (Scoping the Future, Cancer Research UK, 2015). Since its introduction into clinical practice 15 years ago, capsule endoscopy has become established as the primary modality for examining the surface lining of the small intestine, an anatomical site previously considered to be inaccessible to clinicians. However, its reliance on peristalsis for passage through the intestine leads to significant limitations, in particular due to the unpredictable and variable locomotion velocity. Significant abnormalities may be missed, due to intermittent high transit speeds that lead to incomplete visualisation of the intestinal surface. Furthermore, each case produces up to 100,000 still images, from which video footage is generated, taking between 30 and 90 minutes for the clinician to examine in its entirety. The procedure is therefore considered both time-consuming and burdensome for clinicians. There is, therefore, in GI endoscopic practice a desperate need for new modalities that are safe, painless, accurate, reliable and disposable, and which require minimal training for practitioners. This project attempts to find the way to adapt the vibro-impact self-propulsion technique into capsule endoscopy, and to explore the feasibility of innovation for the next generation of endoscopy: the self-propelled capsule endoscopy. Dr Yang Liu is an early-career researcher with a research background in applied dynamics and control, who has focused on developing this self-propulsion technique for different engineering systems. The nature of his applied research urges him to transfer any research findings into practical applications, and capsule endoscopy is one of the core deliverable areas for which the technique can make a revolutionary breakthrough. This requires to equip Dr Liu with the necessary clinical experience and knowledge to transfer his engineering research technologies into the healthcare domain. Therefore, the aims of the proposed discipline hop are: (1) to widen his healthcare technologies' expertise and clinic experience, (2) to embed his research into healthcare technologies, (3) to initiate the development work of the self-propelled capsule endoscopy, and (4) to build a long-lasting working relationship with clinicians, initially in the local NHS hospital, and later worldwide. The approach for Dr Liu to realize this ambitious goal is: 1) to undertake a 6 month discipline hop, learning, observing and being trained in the Endoscopy Department at the Royal Devon & Exeter NHS Foundation Trust; 2) to refine research questions and develop potential solutions; and 3) to fully understand the development cycle of implementing such technique in healthcare sector, including the key activities at the stages of Translational Development, Clinical Evaluation and Regulatory Approval, and Adoption and Diffusion.

Recently, we have pioneered a portfolio of revolutionary optical technologies in the area of laser spectroscopy, namely deep Raman spectroscopy, for non-invasive molecular probing of biological tissue. The developments have the potential of making a step-change in many fields of medicine including cancer diagnosis. The techniques comprise spatially offset Raman spectroscopy (SORS) and Transmission Raman (both patented by the applicants). The methods are described in detail in a tutorial review: http://pubs.rsc.org/en/content/articlelanding/2016/cs/c5cs00466g. There is an urgent clinical need for early objective diagnosis and prediction of likely treatment outcomes for many types of subsurface cancers. This is not addressed by existing technologies. There are numerous steps along the cancer clinical pathway where real-time, in vivo, molecular specific disease analysis would have a major impact. This would significantly reduce needle biopsy, in around 80% of those recalled following mammographic screening this step is unnecessarily - ie leading to the diagnosis of benign lesions. Our novel approach would allow for more accurate and immediate diagnosis in conjunction with mammography at first presentation by improving screening or surveillance techniques, leading to earlier diagnosis and better treatment outcomes. Secondly it would allow surgical margin assessment and treatment monitoring in real-time and thirdly identification of metastatic invasion in the lymphatic system during routine surgery. There are numerous other areas where a rapid molecular analysis of a tissue sample in the clinic or theatre environment would allow improved clinical decision-making, for example when pre- operatively staging the disease and particularly when non-invasively monitoring tumour response during chemo/radiotherapy. Clearly these approaches would be beneficial to the patient by reducing cancer recurrence rates; but also by minimising the numbers of invasive procedures required, thus reducing costs and patient anxiety. Raman spectroscopy is a highly molecular-specific method, which itself has proven to be a useful tool in early epithelial cancer diagnostics, although in its conventional form it has been restricted to sampling the tissue surface of much less than 1 mm deep. The new technology unlocks unique access to tissue abnormalities of up to several cm's deep, i.e. at depths one to two orders of magnitude higher than those previously possible with Raman. Following on from our previous project, where we were able to demonstrate conceptually a ~100x improvement in signal recovery compared to our early feasibility work, we are now able to rapidly develop a platform for real-clinical tools using this approach. We propose to make major breakthroughs in this area and advance diagnostics particularly focussed on breast cancer and lymph node metastasis initially as focused case studies and then potentially applied to prostate cancers (outside the scope of this proposal). This will be explored as a joint cross-disciplinary research venture between Profs Stone and Matousek, the two key researchers in this area. We now seek funding to progress this work in a timely manner by developing a novel medical diagnostic platform of major societal impact. We propose to bring together key players from multidisciplinary areas covering physical sciences, spectroscopy, radiology, cancer diagnostic and therapeutic surgery, and histopathology to exploit all of the relevant skills and develop a critical mass of expertise to tackle these challenging issues.