
University Hospital Coventry
University Hospital Coventry
13 Projects, page 1 of 3
assignment_turned_in Project2021 - 2025Partners:Medici Medical Practice, University of Reading, Massachusetts Institute of Technology, UBC, UCL +37 partnersMedici Medical Practice,University of Reading,Massachusetts Institute of Technology,UBC,UCL,Imperial College London,Draper & Dash Healthcare,Draper & Dash Healthcare,Massachusetts Institute of Technology,UCL Hospitals NHS Foundation Trust,Oxford Immune Algorithmics,SU,Karolinska Institute,Massachusetts Institute of Technology,University Hospital Coventry,Diabetes Digital Media,KI,University College London Hospital (UCLH) NHS Foundation Trust,University of Kent,Cambridge University Hospitals NHS Foundation Trust,UNIVERSITY OF READING,CUH,Diabetes Digital Media,Humanity Vision Limited,Humanity Vision Limited,Addenbrooke's Hospital,Royal Berkshire NHS Foundation Trust,University Hospital Coventry NHS Trust,[no title available],Oxford Immune Algorithmics,University of Reading,Sensyne Health,Stanford University,Stanford University,RBFT,Sensyne Health,University Hospital Coventry NHS Trust,CUHK,University College London Hospital (UCLH) NHS Foundation Trust,RBFT,University of Kent,Medici Medical PracticeFunder: UK Research and Innovation Project Code: EP/W000652/1Funder Contribution: 800,898 GBPThere is an extremely high demand for laboratory-based blood tests from community settings in the UK and analysis suggests an important role in the future for remote blood monitoring that would enable patients and health professionals to carry out their own tests remotely, greatly benefiting patients and speeding up decision making. The COVID-19 pandemic has further highlighted the need for remote and connected blood testing that is beyond the online virtual clinics in the NHS outpatient setting. In current blood testing services for community healthcare, it is challenging to obtain and process blood samples outside of the clinical setting without training and lab facilities, and patients are required to attend a GP surgery or hospital for tests with travel burden and infection risk. Many blood analyses are done in batches that take a long time to build up, meaning the speed of blood sample analysis of routine tests and time taken for diagnosis are further challenges. Despite recent innovations in point of care, current blood analysis tools in practice are mainly mechanical or labour-intensive that require extensive filtering and manual tweaking and not suitable for regular at-home monitoring and longitudinal analytics. There is no personalised real-time approach available to inform disease complexity and conditions over time, which are critical for early detection of acute diseases and the management of chronic conditions. In England, around 95% of clinical pathways rely on patients having access to efficient, timely and cost-effective pathology services and there are 500 million biochemistry and 130 million haematology tests are carried out per year. This means inefficient and infrequent blood testing leads to late diagnosis, incomplete knowledge of disease progression and potential complications in a wide range of populations. Taking those challenges into account and current digital transformation in healthcare, this is a timely opportunity to bring researchers, clinicians and industrialist together to address the challenges of blood monitoring and analytics. The proposed Network+ will build an interdisciplinary community that will explore future blood testing solutions to achieve remote, inclusive, rapid, affordable and personalised blood monitoring, and address the above challenges in community health and care. To achieve the Network+ vision, research of technologies will be conducted from collaborations among information and communication technology (ICT), data and analytical science, clinical science, applied optics, biochemistry, engineering and social sciences in the Network+. The network will address three key technical challenges in blood testing: Remote monitoring, ICT, Personalised data and AI in a range of examplar clinical areas including cancer, autoimmune diseases, sickle cell disease, preoperative care, pathology services and general primary care.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2023 - 2025Partners:Abbott, University Hospital Coventry, Abbott, University Hospital Coventry NHS Trust, Guy's and St Thomas' NHS Foundation Trust +5 partnersAbbott,University Hospital Coventry,Abbott,University Hospital Coventry NHS Trust,Guy's and St Thomas' NHS Foundation Trust,University Hospital Coventry NHS Trust,GUY'S & ST THOMAS' NHS FOUNDATION TRUST,UEA,Guy's and St Thomas' NHS Foundation Trust,Guy's and St Thomas' NHS Foundation TrustFunder: UK Research and Innovation Project Code: EP/X023826/1Funder Contribution: 274,956 GBPMinimally invasive cardiac surgeries are the common treatment for cardiovascular disease, involving the insertion of flexible devices (e.g. catheters or stents) into heart chambers. X-ray fluoroscopy is currently used to guide surgeons as the devices are highly visible under X-rays and modern X-ray systems provide real-time (i.e. with no lag) imaging, a large field-of-view and excellent image resolution. However, X-ray images offer very little anatomical information as surgeons cannot see where the heart chamber is and its surrounding blood vessels, unless contrast agents are injected. Furthermore, X-ray images are 2D images and so objects inside the image could overlap each other making it difficult to determine the accurate position of devices relative to the complex heart anatomy. This results in extended procedure times and thus additional harmful radiation doses. To add this anatomical information, hybrid guidance systems have been developed which combine the X-ray information with other information (e.g. from computerised tomography) to add the shadows or contours on the top of the X-ray images. The drawbacks of these systems are that they still heavily rely on X-ray fluoroscopic images to provide guidance, and all information is still 2D. The aim of this project is to develop a new 3D hybrid guidance system superior to these existing approaches. It will provide 3D information to surgeons, increasing their efficiency and thus reducing X-ray exposure. It will also use additional 3D guidance equipment such as the electroanatomical mapping (EAM) system to reduce the frequency of X-ray images, and so further reduce X-ray exposure. The EAM system uses a weak magnetic field rather than harmful X-ray radiation and so it can be switched on throughout the procedure. The primary use of the EAM system is to map electrophysiological activities within the heart. But it also can track catheters within a heart chamber and create low-resolution 3D models of heart chambers. It is not possible to visualise the 3D blood vessel structures clearly when using the EAM system and also some of devices such as stents and balloons might not be tracked. Hence the need for the proposed hybrid system with X-ray information. To develop this system we will use advanced computer vision techniques to detect devices and extract 3D blood vessel models from X-ray images, and then fuse these with existing 3D models inside the EAM system to provide the completed information to guide the procedure. Due to the high-level of noise present in low-dose X-ray images and the interference from overlapping objects, it is a challenging task to achieve accurate and robust detection in real-time. To meet the challenges, a novel approach is proposed to simultaneously detect the electrode catheters by the electrode pattern and the device on the wire by an image classifier. Since all devices are objects attached to the wires, our learning-base image classifiers will only need to search the areas along the wire-like objects. Furthermore, our approach will also be able to solve the challenge of the accurate alignment between 3D models in two systems measured in different coordinate systems. The alignment is based on tracking the 3D position of the same device in both an EAM system and an X-ray system. As it is possible to use the EAM system as the main guidance tool and use less frequent X-ray images, our proposed system will significantly reduce X-ray radiation exposure. This will benefit patients as X-ray radiation might cause the cancer in their later life. We will partner with Abbott Medical UK Ltd, and aim to develop and adapt our approach using Abbott's EAM system so that a research prototype can be made in the near future. But our theoretical contributions will not limited to the EAM system, and could be used to hybridise X-ray images with other image-guidance systems, such as the 3D echo imaging, as well as future robotic surgery systems.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2014 - 2016Partners:University Hospital Coventry NHS Trust, University Hospital Coventry, GE Healthcare, General Electric (United Kingdom), University Hospital Coventry NHS Trust +8 partnersUniversity Hospital Coventry NHS Trust,University Hospital Coventry,GE Healthcare,General Electric (United Kingdom),University Hospital Coventry NHS Trust,The University of Arizona,Autonomous University of Barcelona (UAB),Autonomous University of Barcelona,University of Warwick,UA,University of Warwick,GE Healthcare,The University of ArizonaFunder: UK Research and Innovation Project Code: EP/L02764X/1Funder Contribution: 97,702 GBPPathology is the branch of medicine that studies the cause, origin, and nature of diseases through the examination of tissue biopsies at a microscopic level. Pathology slides are traditionally handled by cutting a tissue sample into paper-thin sections, and staining them so to bring out regions of interest (RoIs). A pathologist places these paper-thin sections on a glass slide under a microscope in order to look for a range of features that aid in confirming the presence and malignancy level of the disease. For example, in the case of cancer biopsies, the pathologist analyses the shape, size and amount of abnormal and normal cell nuclei in the tissue to confirm the existence and progression of the tumour. Recent advances on whole-slide digital scanners have made possible the digitization of pathology slides, allowing their storage and manipulation in digital form. The digitized versions of pathology slides, which are called virtual slides or whole-slide images (WSIs), are complementing traditional analysis techniques that rely on pathologists looking under a microscope with techniques that rely on pathologists looking at digital images on a monitor. Moreover, digitization of these slides also allows providing telepathology services by sharing WSIs and thus reaching isolated hospitals and medical centres. For example, thanks to telepathology, pathologists would be able to send WSIs electronically to others or post them on a secure web-site making them available for consultation with other pathologists. As a consequence, more pathologists may be brought into the process of making a diagnosis, thus avoiding medical errors. Due to the high resolution required to digitize pathology slides, the resulting WSIs tend to be huge in file size, which results in heavy demands for storage and transmission resources. For example, the digitization of a single core of prostate biopsy tissue, of roughly the dimensions of a stamp, could easily result in 900 million pixels. By comparison, a photograph of 4x5 inches in size scanned at 300 dots per inch, which is the standard resolution for printing in a magazine, results in only 1.8 million pixels. So, WSIs usually require around 500 times more pixels than regular digital images. Moreover, a single pathology study normally comprises more than one biopsy sample. For example, in the case of prostate cancer studies, more than 10 biopsy samples are often required per patient, resulting in hundreds of gigabytes of imaging data per study. As a consequence, the main challenge that currently prevents telepathology from being widely used in clinical settings is the huge file size of WSIs, which makes the access and transmission of these data over different channels lengthy. Additionally, their huge file size also prevents WSIs from being widely used in current Picture Archiving and Communications Systems (PACS), which comprise a collection of software and network infrastructure used in hospitals and medical centres to store, share and display medical images. Integrating WSIs into PACS would allow pathologist to use other patient data available in PACS in order to increase the accuracy of diagnosis. Therefore, designing efficient coding methods capable of facilitating the access and transmission of WSIs for telepathology applications, while allowing integrating these data into PACS, remains a challenge. This project is mainly concerned with the design of such methods.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2016 - 2021Partners:University Hospitals Birmingham NHS FT, Imperial College Healthcare NHS Trust, Royal Free London NHS Foundation Trust, NHS Greater Glasgow and Clyde, Barts Health NHS Trust +35 partnersUniversity Hospitals Birmingham NHS FT,Imperial College Healthcare NHS Trust,Royal Free London NHS Foundation Trust,NHS Greater Glasgow and Clyde,Barts Health NHS Trust,St George's Uni Hospitals NHS Fdn Trust,Cardiff and Vale University Health Board,University Hospital Coventry,North Bristol NHS Trust,University of Southampton,Lancashire Teaching Hospitals NHS Foundation Trust,University of Bristol,University Hospitals Birmingham NHS Foundation Trust,SGUL,UCL,Leeds Teaching Hospitals NHS Trust,Great Ormond Street Hospital,Oxford Uni. Hosps. NHS Foundation Trust,University of Hull,Walton Centre,brainstrust,Walton Centre Neurology/Neurosurgery,South Tees Hospitals NHS Foundation Trust,NHS GREATER GLASGOW AND CLYDE,University of Southampton,University of Cambridge,NHS Lothian,Salford Royal NHS Foundation Trust,Hull Univ Teaching Hospitals NHS Trust,Sheffield Teaching Hospitals NHS Foundation Trust,UCL Hospitals NHS Foundation Trust,University of Edinburgh,[no title available],WLMHT,Nottingham University Hospitals NHS Trust,Cambridge Uni Hosp Trust (to be replaced,Oxford University Hospitals NHS Trust,King's College Hospital,Royal Free London NHS Foundation Trust,BHR University Hospitals NHS TrustFunder: UK Research and Innovation Project Code: MR/N004272/1Funder Contribution: 542,090 GBPNeurological diseases cause a substantial and increasing personal, social and economic burden. Although there have been exceptions, there is increasing frustration at the limitations of learning from animal models, emphasising the importance of studying human tissue. Neuropathologists work in NHS hospitals examining samples from the brain and related tissues derived from operations (biopsies) or post mortem examinations. Their job is to identify abnormalities, make a diagnosis and try to understand how the abnormalities arise. Neuropathology has existed as a specialty in the UK for 40-50 years and, as a consequence of this work, substantial archives of diagnostically verified tissue have been established nationwide. These archives contain a wealth of tissue from a great variety of neurological conditions, including common conditions such as stroke, head injury, tumours, infections, psychiatric disorders, developmental disorders and many rare conditions, and represent an underutilised resource for research. BRAIN UK (the UK BRain Archive Information Network) networks the tissue archives of neuropathology departments based in 26 regional NHS Clinical Neuroscience Centres to form a virtual brain bank, acting as a "matchmaker" linking researchers needing tissue to the appropriate samples. Through BRAIN UK researchers can gain access to >400,000 samples from a wide range of diseases affecting the brain, spinal cord, nerve, muscle and eye. BRAIN UK has ethical approval which covers the majority of projects, saving the researchers considerable time as they would otherwise have to obtain this approval independently. Over the past 4 years BRAIN UK has supported 48 research projects in many centres around the UK and overseas. In the coming 4 years we want to continue to provide tissue to researchers from existing resources and add newly obtained samples of which >16,000 are becoming available each year. We also aim to gather the results of researchers' studies performed on tissue obtained through BRAIN UK to form a central register of findings which will benefit new researchers wanting to perform new studies on these tissue samples. Finally, we will link BRAIN UK with UK Biobank, which has 500,000 intensively studied participants from the general population, in order to learn more about the origins of neurological disease. As far as we are aware, the BRAIN UK network is unique in the world and is very economical as it makes use of tissue samples already being stored in NHS archives which would otherwise be unused and unavailable to researchers.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2021 - 2025Partners:University Hospital Coventry NHS Trust, University Hospital Coventry, University Hospital Coventry NHS Trust, University of Warwick, University of Warwick +3 partnersUniversity Hospital Coventry NHS Trust,University Hospital Coventry,University Hospital Coventry NHS Trust,University of Warwick,University of Warwick,National Farmers Union,NFU,NFUFunder: UK Research and Innovation Project Code: EP/V002236/1Funder Contribution: 1,288,650 GBPThis fellowship programme will take a circular economy (CE) approach and unlock the huge potential of renewable biomass, which can be easily sourced from agriculture/aquaculture/food industry as byproducts or wastes. The biomass contains biopolymers cellulose, chitin/chitosan, starch, protein, alginate and lignin, which are valuable resources for making environmentally friendly materials. Moreover, these biopolymers have unique properties and functions, which make them highly potential in important, rapidly growing applications such as therapeutic agent delivery, tissue engineering scaffolds, biological devices, green electronics, sensing, dye and heavy metal removal, oil/water separation, and optics. However, enormous challenges exist to process biopolymers and achieve desired properties/functions cost-effectively; these valuable biomass resources have long been underutilised. This proposed ambitious and adventurous research will focus on the smart design of materials formulation and engineering process from an interdisciplinary perspective to realise the assembly of biopolymer composite materials under a single flow process. This will eventually lead to a reinvented, cost-effective engineering technology based on 3D printing to produce a diverse range of robust, biopolymer composite materials with tailored structure, properties and functionality. Due to the versatile chemistry of biopolymers for modification, the bespoke 'green' materials are expected to outperform many synthetic polymers and composites for specific applications such as tissue engineering and controlled release. The outcomes of this transformative project will not only provide fundamental knowledge leading to a completely new line of research, but also deliver ground-breaking technologies that will impact the UK's plastic industry by providing truly sustainable and high-performance options for high-end technological areas (e.g. healthcare and agriculture).
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