Dementia is one of the foremost global healthcare challenges of our times, with a vast physical, psychological, social and economic impact. About 50 million people worldwide live with dementia today and this number is expected to triple by 2050. The direct medical and social care costs have been estimated in 2015 to be 1.4% of gross domestic product in high-income countries. Since 2016, dementia has become the leading cause of deaths in the UK. Even today, in the midst of an unprecedented pandemic, Covid-19 is hitting people with dementia the hardest, with dementia being the most common pre-existing condition for people dying of Covid-19. Moreover, patients recovered from Covid-19 are thought to be at significantly increased risk for dementia later in life. Indeed, the need to find a cure for dementia has never been greater. However, till this day, no disease-modifying treatments have been developed. The attrition rate of treatment trials for Alzheimer's disease (AD), the commonest cause of dementia, is high, with 98% failing in Phase III. The reason, at least in part, for the successive, and often costly, trial failures is likely to be that the disease is targeted too late, after significant and irreversible loss of brain cells has occurred. Hence, there is now a consensus and drive towards undertaking trials of therapies much earlier, before the disease becomes irreversible. This calls for the development of more advanced biomarkers that can offer increased sensitivity for identifying individuals at early stages of the disease. University College London (UCL) is an international leader in the development of cutting-edge MRI biomarkers for early diagnosis of dementia. Dr Michele Guerreri, the proposed secondee, is an early-career imaging researcher at UCL who has made significant progress in our quest towards better biomarkers for dementia. This secondment is driven by the aspiration to translate this cutting-edge research into a product to accelerate its real-world impact. AINOSTICS, the hosting organization, is an award-winning imaging AI start-up which specialises in combining cutting-edge MRI and other biomarker data with state-of-the-art artificial intelligence (AI) to detect early changes in AD and other neurodegenerative diseases. They thus represent a perfect partner to deliver this project. This project will boost the skills of Dr Guerreri which will be working in a cutting-edge AI environment, learning the translation process from a research idea into a commercial product. The Secondee will have the opportunity to experience the commercial sector for the first time, learning the development and commercialisation process of his academic research. The hosting organisation will benefit from the Secondee's expertise in the development of state-of-the-art imaging biomarkers for dementia. In the long term, the project will lay the groundwork for a close collaboration between UCL and AINOSTICS. The expertise gained by Dr Guerreri will allow him to develop similar relationships with other players in the vibrant UK imaging AI sector, closing the traditional gap that has been preventing or slowing the knowledge exchange between biomedical industry and academia.

Analysis of conditions altering microstructural tissue integrity & cellular arrangement, such as dementia, is vital to personalise patient treatment & improved outcomes. Presently, it is often the case that these subjects are only identified after the disease is at a grossly advanced stage, making prognosis poor. In this fellowship I aim to develop an innovative AI-enabled magnetic resonance (MR) image analysis platform, known as QUANTIMA, capable of generating new non-invasive biomarkers of brain microstructure & providing non-invasive tools for improved diagnostic information as powerful as invasive and/or expensive techniques such as CSF lumbar puncture & PET. Understanding the design (morphology) & arrangement (tissue microstructure) of the brain's individual components is the key to deciphering both its structure & function, and more importantly its degeneration/dysregulation in diseases. The platform will make use of advanced diffusion-weighted magnetic resonance imaging (DW-MRI) based microstructure modelling techniques, providing an indirect but non-invasive probe of the tissue microstructure at the micrometre scale. However, tissue microstructure is highly complex while the DW-MRI signal is quite simple, so the mapping from signal to microstructure is ill-posed. Current computational modelling techniques aimed at overcoming this challenge use mathematical models, mapping the DW-MRI signal to underlying tissue properties, to estimate those properties by fitting the models per voxel of the DW-MRI data. Nevertheless, these methods suffer from a number of limitations that have restricted their diagnostic power & clinical adoption, such as poor sensitivity to features of complex cellular morphologies, requirements for long MRI scan times & state-of-the-art hardware not commonly available in clinical settings, reliance on predefined models of cellular arrangement & biology trying to mimic healthy tissue, therefore limiting the methods' applicability to disease processes (especially unobserved), & unquantified ambiguities. To overcome these limitations, the proposed platform will use advanced AI-based optimisations & accelerations, capable of generating quantitative estimates of tissue microstructure, comparable with the state-of-the-art in microstructure computational modelling, whilst overcoming their limitations by: i) relying on clinically achievable MR acquisition protocols applicable on commonly available MR hardware (1.5T & 3T scanners), ii) providing a model free approach that solely relies on the observations made using the diffusion MR signal, iii) is capable of estimating uncertainty, quantifying ambiguity & the significance of the results. When diagnosing these conditions, aside from imaging, doctors rely on information gathered from the patient's medical history, physical examination, laboratory tests, and the characteristic changes in thinking, day-to-day function, & behaviour. Offering a unified solution, the platform will also be able to perform multimodal & multi parametric data fusion, utilising information from such data sources (e.g.,cognitive tests), allowing for earlier & accurate dementia diagnosis, subtyping, staging, and disease-trajectory prediction; therefore, enabling personalised treatment selection & improved outcomes. The project will also lead to the development of a multiparametric dementia-specific optimised (for clinical use) MRI scan protocol, maximising information obtained over short scans & satisfying the requirements of the novel AI-enabled microstructure modelling technique that I intend to develop. In collaborations with the London AI Centre for Value Based Healthcare, University of Manchester, Greater Manchester Mental Health NHS Foundation Trust, GSK, and Salford Royal NHS Foundation Trust, the platform will then be validated using a multifaceted approach: using both retrospective & prospective patients data, and through a clinical pilot study.

Digital Health technologies can make a positive difference to the outcomes of patient treatment, management and care. Improving digital services and the sharing and use of data will also save time and resources so that staff can better focus on delivering medical and social care. Examples of such technologies include data collected through smartphones. For example, the ZOE COVID Symptom Study App used during the pandemic was jointly developed by King's College London, and now has more than four million users. Other digital technologies include wearable devices which can help monitor heart rate, activity and sleep and remotely assess and help manage a wide range of conditions. For example, the £23M Innovative Medicines Initiative RADAR-CNS led by King's has pioneered their use in depression, multiple sclerosis and epilepsy. Our aim is to enable the development of new digital technologies and reduce the time it takes for these to benefit patient care. The King's Health Partner (KHP) Digital Health Hub will do this by helping researchers, health and social care staff, patients and industry to work together better. We also hope to increase the availability of such technologies nationally by offering support to enable new businesses to grow rapidly, thereby making a more immediate difference to patients' lives. Digital health technologies have lots of potential but their widespread use is limited by: - A lack of examples of how clinicians, academics, engineers, quality assurance experts, health economists, patients and end users can best work together during development - Specific gaps in training and knowledge amongst the different groups, for example: - Academic and industry technologists may have trouble understanding NHS systems and fail to engage with the end users of the technologies they are trying to develop, such as health care providers, patients and carers. They may not know about or understand the complex regulatory pathway which needs to be followed before such technologies can be used in clinical practice. - Clinical specialists may lack the appropriate technical skills such as data analyses, coding and programming languages to help them develop digital applications they think will be helpful to their patients. The KHP Digital Health Hub will help to overcome the barriers to the rapid development and use of digital technologies nationally. It will be an accessible "ecosystem" comprising specialists from different sectors working together to improve understanding and use of digital technologies and addressing the government's long-term goals for health and social care. With our partners, we will connect the digital health research community to the substantial opportunities for investment in London and our diverse and world leading healthcare research environment. We have brought together a wealth of expertise from across KHP, including King's College London and partner NHS Trusts, patients and industry collaborators, to provide support and training, and create opportunities for the acceleration of digital health across the UK. KHP includes seven mental health and physical healthcare hospitals and many community sites with ~4.8 million patient contacts each year and a combined annual turnover of more than £3.7 billion. The KHP Digital Health Hub will provide: - proven expertise, infrastructure and experience of co-creation and commercialisation - a three-way clinical, academic and industry partnership - a physical location where technology developers can work collaboratively, and - an excellent track record in training which will be offered to all our partners across the health and social care sectors. With the right support and networks in place, digital health technologies have the power to transform patient care and experiences across the UK. The knowledge and expertise is all there, and together we can make sure it is shared, translated and built upon, at every step of the way.