AICCET will enable better prognostics, the process by which disease is diagnosed and the outcome of treatment predicted. This allows for improved treatment planning, a pre-requisite for a better patient journey and experience. It does this by avoiding unnecessary hospital in-patient care, enabling outpatient procedures using technologies which do not require highly specialised personnel such that clinics can move closer to the community they serve. This move away from hospital based healthcare is further enabled through monitoring patient health at home and managing chronic disease with solutions provided to the GPs, all moving healthcare further into the community. Examples of such interventions include: 1. Inch-worm technology provides a solution which reduces risk to patients, makes the device easier to use by NHS staff, and moves the procedure away from a costly secondary care location performed by specialised clinicians into a community out-patient setting. Public Patient Involvement and Engagement (PPIE) is particularly important for accelerating the impact of this technology, with significant potential for reducing healthcare inequalities as traditional endoscopy has reduced efficacy in certain patient phenotypes (high BMI, infirm or living with disability) and colonoscopy screening has reduced uptake in those with a lower socio-economic background due to difficulties accessing transport to a central hospital or from fear of the current procedure - leading to late diagnosis and poor prognoses. (Manfredi - University of Dundee). 2. Power frugal algorithms used in wearable electronics for the detection of scabbing in sheep can be applied to outpatients in the community who are soon to have invasive surgery. It has been shown that exercise prior to surgical operation helps positive outcome of surgery. The device will help reduce visit hospitals during the pre-habilitation and also post-habilitation phase allowing doctors to monitor remotely the progress made by the patients as they undergo prescribed training exercises. The AICCET consortium has gathered 14 organisations (5 universities, a further education college, 5 civic bodies, 1 hospital and 2 NHS organisations in the Tayside region to address the challenges of community healthcare. This consortium is in direct alignment with the "shift left" taking place in the continuum of care enabled by technological and process solutions that offer the highest quality of life for patients through the simultaneous provision of improved quality of healthcare and reduced costs of provision. AICCET is putting in place the mechanisms to accelerate impact in community healthcare of innovative research created by these 5 universities. AICCET sets up the foundations of a healthcare technology ecosystem addressing the barriers for research and innovation and increasing the connectivity to accelerate impact in community healthcare.

Medical device technologies are vital for the detection and treatment of a great number of diseases and healthcare problems. Increasingly, micro-devices are being developed for minimally-invasive measurement and therapy, for example in cancer detection and drug delivery. To enable broad-based takeup of such devices it is vital to provide low-cost and reliable manufacturing solutions. The group at Heriot-Watt has significant experience in developing manufacturing solutions for a wide range of applications, with a particular focus in recent years on medical devices e.g. for cancer detection and treatment. Particular challenges include: miniaturisation to enable minimally invasive application; the low-cost integration of optical, chemical and electronic technologies; and hermetic sealing to prevent unwanted ingress of fluids, whilst allowing appropriate interaction e.g. measurement of cell stiffness, measurement of pH, laser ablation/treatment of cancerous tissue. Our manufacturing expertise (spanning laser techniques such as ablation, sintering, bonding and inscription; also additive and subtractive microfabrication processes based on mechanical, chemical, evaporative and microwave techniques), coupled with our highly supportive and growing base of clinical and industrial partners means that we are ideally placed to provide appropriate manufacturing solutions, and to enable rigorous testing and a route to commercialisation and ultimate application. The Platform will allow us to retain key staff, and to deploy them in ways that are not possible with standard proposals. In particular, we will be able to accelerate our ability to grasp immediate opportunities based on our existing collaborations, both within the group and with external partners, by carrying out critical proof-of-concept studies. The PDRAs employed will benefit greatly from the enhanced career development under the Platform. We will broaden their experience through research exchanges; engage them in proposals to win new funding; support them in applications for personal fellowships; provide them with dedicated funds for their own short proof-of-concept projects (10% of budget allocated to PDRA-led 'seedcorn' projects); provide a mentoring programme using industrial and academic members of our Advisory Board; and involve them in management of the Platform. We will organise facilitated workshops to bring together a broader group of academics and medics and to identify new collaborative activity and application areas. We will also employ targeted dissemination activity to inform current and potential industrial and clinical partners of the full range of our medical device manufacturing research activity.

Heart disease is the leading cause of disability and death in the UK and worldwide, resulting in enormous health care costs. Risk prediction on an individual patient basis is imperfect. Advanced medical development has already saved many lives, particularly in systolic heart failure. However, there is currently no treatment option for diastolic heart failure (with preserved ejection fraction) due to its complexity of multiple mechanisms and co-modality. Structural heart diseases, such as myocardial infarction (MI- commonly known as heart attack) and mitral regurgitation (MR, a leakage of blood through the mitral valve to left atrium in systole), where biomechanical factors are crucial, are often precursors to heart failure. MI can eventually lead to dilated heart failure despite immediate treatments post-MI. MR can induce pulmonary hypertension and oedema and subsequently, right heart overload and heart failure. The grand challenge is for these situations the heart simply cannot be modelled as an isolated left ventricle (as in most of the current studies); flow-structure interaction (FSI), heart-valve interaction, multiscale soft tissue mechanics, and tissue growth and remodelling (G&R) all play important roles in the progression of the structural diseases. This project is set up to meet this challenge by delivering a multiscale computational framework to include Whole-Heart FSI with G&R. Making use of the novel mathematical tools (constitutive laws, G&R, upscaling and statistical inference) developed by SofTMech, I will build a realistic four-chamber heart model that include heart-valve, chamber-chamber, heart-blood, and heart-circulation interactions, which will be powerful enough to model MI, MR and their pathological consequences. This work will be in close collaboration with my clinical, industrial and academic collaborators. The model will quantify which factors lead to adverse G&R and what variations are to be expected as the disease progresses. We will also identify significant biomechanical markers (e.g. constitutive parameters, energy indices, stress/strain evolution). The predictive values of these biomechanical parameters will be assessed against other established predictors of adverse remodellings, such as duration of ischaemia, final coronary flow grade after a primary percutaneous coronary intervention, and microvascular obstruction revealed by MRI. Thus, this project will generate new testable hypotheses and will be a significant step up towards more consistent decision-support for clinicians, since increasingly the pace and complexity of medical advances outstrip the ability of individual clinicians to cope with. Due to the statistical emulation and uncertainty quantification built into the project, the model predictions will be fast and quantified with error bounds on the outcome of alternative treatments. Consequently, we will also address the critical aspect of convincing clinicians that information obtained from simulations will be correct and relevant to their daily practice. The proposed research is right within the Healthcare Technologies "Optimising Treatment" and "Developing Future Therapies" priority areas, as well as targeting "New Connections from Mathematical Sciences", and "Statistics and Applied Probability" of Mathematical Sciences.

The EMERGENCE network aims to create a sustainable eco-system of researchers, businesses, end-users, health and social care commissioners and practitioners, policy makers and regulatory bodies in order to build knowledge and capability needed to enable healthcare robots to support people living with frailty in the community. By adopting a person-centred approach to developing healthcare robotics technology we seek to improve the quality of life and independence of older people at risk of, and living with frailty, whilst helping to contain spiralling care costs. Individuals with frailty have different needs but, commonly, assistance is needed in activities related to mobility, self-care and domestic life, social activities and relationships. Healthcare can be enhanced by supporting people to better self-manage the conditions resulting from frailty, and improving information and data flow between individuals and healthcare practitioners, enabling more timely interventions. Providing cost-effective and high-quality support for an aging population is a high priority issue for the government. The lack of adequate social care provisions in the community and funding cuts have added to the pressures on an already overstretched healthcare system. The gaps in ability to deliver the requisite quality of care, in the face of a shrinking care workforce, have been particularly exposed during the ongoing Covid-19 crisis. Healthcare robots are increasingly recognised as solutions in helping people improve independent living, by having the ability to offer physical assistance as well as supporting complex self-management and healthcare tasks when integrated with patient data. The EMERGENCE network will foster and facilitate innovative research and development of healthcare robotic solutions so that they can be realised as pragmatic and sustainable solutions providing personalised, affordable and inclusive health and social care in the community. We will work with our clinical partners and user groups to translate the current health and social care challenges in assessing, reducing and managing frailty into a set of clear and actionable requirements that will inspire novel research and enable engineers to develop appropriate healthcare robotics solutions. We will also establish best practice guidelines for informing the design and development of healthcare robotics solutions, addressing assessment, reduction and self-management of frailty and end-user interactions for people with age-related sensory, physical and cognitive impairments. This will help the UK develop cross-cutting research capabilities in ethical design, evaluation and production of healthcare robots. To enable the design and evaluation of healthcare robotic solutions we will utilize the consortium's living lab test beds. These include the Assisted Living Studio in the Bristol Robotics Lab covering the South West, the National Robotarium in Edinburgh together with the Health Innovation South East Scotland's Midlothian test bed, the Advanced Wellbeing Research Centre and HomeLab in Sheffield, and the Robot House at the University of Hertfordshire covering the South East. Up to 10 funded feasibility studies will drive co-designed, high quality research that will lead to technologies capable of transforming community health and care. The network will also establish safety and regulatory requirements to ensure that healthcare robotic solutions can be easily deployed and integrated as part of community-based frailty care packages. In addition, we will identify gaps in the skills set of carers and therapists that might prevent them from using robotic solutions effectively and inform the development of training content to address these gaps. This will foster the regulatory, political and commercial environments and the workforce skills needed to make the UK a global leader in the use of robotics to support the government's ageing society grand challenge.

There have recently been impressive developments in the mathematical modelling of physiological processes. As part of a previously EPSRC-funded research centre (SofTMech), we have developed mathematical models for the mechanical and electrophysiological processes of the heart, and the flow in the blood vessel network. This allows us to gain deeper insight into the state of a variety of serious cardiovascular diseases, like hypoxia (a condition in which a region of the body is deprived of adequate oxygen supply), angina (reduced blood flow to the heart), pulmonary hypertension (high blood pressure in the lungs) and myocardial infarction (heart attack). A more recent extension of this work to modelling blood flow in the eye also provides novel indicators to assess the degree of traumatic brain injury. What all these models have in common is a complex mathematical description of the physiological processes in terms of differential equations that depend on various material parameters, related e.g. to the stiffness of the blood vessels or the contractility of the muscle fibres. While knowledge of these parameters would be of substantial benefit to the clinical practitioner to help them improve their diagnosis of the disease status, most of the parameters cannot be measured in vivo, i.e. in a living patient. For instance, the determination of the stiffness and contractility of the cardiac tissue would require the extraction of the heart from a patient and its inspection in a laboratory, which can only be done in a post mortem autopsy. It is here that our mathematical models reveal their diagnostic potential. Our equations of the mechanical processes in the heart predict the movement of the heart muscle and how its deformations change in time. These movements can also be observed with magnetic resonance image (MRI) scans, and they depend on the physiological parameters. We can thus compare the predictions from our model with the patterns found in the MRI scans, and search for the parameters that provide the best agreement. In a previous proof-of-concept study we have demonstrated that the physiological parameters identified in this way lead to an improved understanding of the cardiac disease status, which is important for deciding on appropriate treatment options. Unfortunately, the calibration procedure described above faces enormous computational costs. We typically have a large number of physiological parameters, and an exhaustive search in a high-dimensional parameter space is a challenging problem. In addition, every time we change the parameters, our mathematical equations need to be solved again. This requires the application of complex numerical procedures, which take several minutes to converge. The consequence is that even with a high-performance computer, it takes several weeks to determine the physiological parameters in the way described above. It therefore appears that despite their enormous potential, state of the art mathematical modelling techniques can never be practically applied in the clinical practice, where diagnosis and decisions on alternative treatment option have to be made in real time. Addressing this difficulty is the objective of our proposed research. The idea is to approximate the computationally expensive mathematical model by a computationally cheap surrogate model called an emulator. To create this emulator, we cover the parameter space with an appropriate design, solve the mathematical equations in parallel numerically for the chosen parameters, and then fit a non-linear statistical regression model to this training set. After this initial computational investment, the emulator thus created gives predictions for new parameter values practically instantaneously, allowing us to carry out the calibration procedure described above in real time. This will open the doors to harnessing the diagnostic potential of state-of-the art mathematical models for improved decision support in the clinic.