The pituitary is a small gland at the base of the brain that produces hormones that control several important bodily functions. Pituitary tumours are one of the most common types of brain tumours, where a symptomatic tumour can cause hormonal imbalances and other health problems. Transsphenoidal surgery is the gold standard treatment for most symptomatic pituitary tumours. This is a minimally invasive surgery as it is performed through the nostrils and nasal sinuses leaving no visible scars from the procedure. Transsphenoidal surgery is challenging and high risk due to the narrow approach and proximity of critical neurovascular structures such as the optic nerves and carotid arteries, resulting in a relatively high rate of complications. The most common of these complications requiring medical or surgical treatment are dysnatraemia (related to pituitary dysfunction), and post-operative cerebrospinal fluid (CSF) rhinorrhoea (related to insufficient repair of the skull base). Thus, leading to increased hospitalization and recovery time with high risk of life-threatening conditions. To reduce the risk of these complications, this research project aims to develop a real-time Artificial Intelligence (AI) assisted decision support framework that can understand the surgical procedure, predict surgical errors and identify intraoperative causes of complications. The AI model will recognise surgical steps, detect surgical instruments, and identify specific instrument-tissue interactions during the sellar phase (for dysnatraemia) and closure phase (for CSF rhinorrhoea) of the surgery. The framework will use multimodal data, including pre- and post-operative clinical data and surgical scene perception, to predict and alert the surgeon of any surgical errors and potential post-operative complications in real-time. By developing this framework, the project aims to improve surgical outcomes by reducing the frequency of post-operative complications, shortening the length of hospital stays, and improving patients' recovery.

We will develop novel, low-cost, diagnostic technologies for the detection of a blinding condition called diabetic retinopathy (DR) in China. These technologies will enable cost-effective, large-scale detection of sight-threatening disease to be performed by non-expert health care workers at the time and place of patient care in China. Over 110 million people in China live with diabetes and this number is expected to increase to 150m by 2040. Diabetes causes visual loss through damage to blood vessels of the retina at the back of the eye. Treatments are effective (and increasingly affordable in China) but only if the disease is detected early enough. Improving early diagnosis and treatment of DR is one of the principal objectives of the Chinese Government's 5-Year National Plan of Eye Health (2016-2020). Current methods of detecting DR rely on costly imaging equipment and many skilled personnel to take and interpret retinal images. China has very few health workers with these skills. Case detection strategies which are cost-effective in Western Europe cannot possibly be replicated at the scale necessary for China. Building on an existing collaboration between the University of Liverpool, Peking University and the Chinese Medical Association (CMA), our joint research team from Liverpool and China of engineers, statisticians, education specialists, eye doctors and health economists are well placed to develop a new diagnostic imaging solution tailored to local needs. Our objectives for this project are: 1. To develop a novel, low-cost, robust imaging device for detection of DR 2. To develop new automated computer algorithms to rate images of the retina for sight-threatening disease 3. To develop a novel comparative judgement method to refine the DR severity grading from automated computer algorithms 4. To validate the new technologies in the UK and test them in China to ensure maximum cost-effectiveness We will develop a new low-cost, camera designed specifically for the needs of China. This device will produce both high-quality colour images and optical coherence tomography (OCT) images of the retina. OCT is widely available but current commercial systems are very expensive. Our new device will be based on our novel patent-pending technology and will be first tested on human donor tissue. We will develop and evaluate new, automated image analysis techniques allowing computers to learn to analyse both colour and OCT images of the retina. We aim to produce systems capable of differentiating between patients with and without DR and between those with mild/moderate and severe disease at the time and place of patient care. In order to achieve a high level of diagnostic accuracy (over and above automated image analysis), we will develop and evaluate a new human learning system. This system will harness the collective judgements of Chinese health workers to rank images in terms of DR severity. We will create a self-sustaining network of activity where novices and experts support each other in making effective clinical judgements. We will develop new statistical methods to underpin the system and to evaluate both diagnostic accuracy and performance of the health workers. Our imaging and diagnostic system will be validated in 241 patients with diabetes in the UK. Our project team in China will then undertake a pilot study of 461 patients and a costing exercise. The diagnostic accuracy of the system and its cost-effectiveness will be investigated. We will engage policy makers through our Chinese team who occupy leading roles in the CMA. Detection and treatment of sight threatening DR will prevent disability with benefit to Chinese society and China's economy. Our systems will upskill health care workers, strengthen existing health systems and build research capacity in China. Dissemination of our techniques through open-source software will maximise benefits for other low and middle-income countries.

Cardiovascular diseases (CVDs) is the second biggest killer in the UK and currently, more than 7 million people are living with CVD in the country. Early identification of individuals with significant risk is critical to improve the patient quality of life and reduce the financial burden on the social and healthcare systems. A large number of CVDs lead to the shortage of blood supply to the heart muscle and abnormal motion, which can be diagnosed non-invasively by analysing the patient's dynamic cardiac imaging data. Manual assessment of these images is subjective, non-reproducible, limited to the left ventricle, and time-consuming. Statistical atlases, describing the 'average' pattern of the heart motion over a large healthy population, can be potentially useful to identify deviations from normality in individuals. However, the integration of the existing atlases into clinical practice is inhibited by three key limitations: (i) the derived motion statistics are often independent of the patient's age, gender, weight, etc. (metadata) that are essential for precise diagnosis, (ii) Being non-probabilistic, these atlases fail to provide a measure of certainty in the extracted motion abnormalities thus their clinical reliability is seriously hampered, (iii) they are often derived using a small number of data sets (n<1000), limiting their statistical power. To alleviate these key limitations, this proposal aims, for the first time, to develop a full probabilistic atlas to accurately evaluate bi-ventricular motion abnormalities by holistically integrating imaging and metadata from a large population cardiac imaging study. BIANDA will be a novel Bayesian approach extending the recent developments in deep recurrent neural networks (RNNs). These networks provide a natural mechanism to model sequential data such as 2D video. Yet, using RNNs to model the complex dynamics of the heart motion is conceptually new and evidently powerful. The motion will be modelled as the spatiotemporal (3D+t) sequence of the heart shapes across the full cardiac cycle, extracted from cine Cardiac Magnetic Resonance (CMR) images. The atlas will be a recurrent model that, given a sequence, it will predict a probabilistic distribution function (pdf) for the next status of the heart. More importantly, the pdf will be conditioned on the patient's metadata. Thus by measuring the spatial deviations from the expected shape at each phase, the atlas will allow very accurate quantification of anatomical and functional cardiac abnormalities (and variances showing uncertainties) specific to the patient's age, gender, age, ethnicity, etc. The PI has an extensive experience in developing Bayesian and non-Gaussian statistical atlases from shapes. However, the previous work (i) was not designed to analyse motion data, (ii) discarded the patient metadata (such as age, gender, ethnicity, etc.), and (iii) did not scale into large populations. Therefore, the atlas was not clinically deployable to study cardiac motion abnormalities, which are relevant to various CVDs. This proposal will significantly depart from the PI's previous by combining Bayesian models with deep neural networks. The former is required to handle uncertainties; the latter will significantly boost the prediction and computational efficiency (using GPUs), thus scalability. The atlas will be derived from the UK Biobank CMR study aiming to scan n>100,000 patients by 2022. The training of the atlas will be pursued as the new releases of the data sets from the UK Biobank becomes available. The PI has established collaboration with the clinical advisor for this study and has full access to the CMR data sets. This is essential for the success of the proposal as the training of deep neural networks requires access to an ample of data sets, a possibility which has emerged only recently. In this regard, BIANDA is timely and promising.

This proposal brings together 19 universities, including 12 out of 16 newly established UKRI CDTs in Artificial Intelligence. Led by the University of Oxford, with support from the Alan Turing Institute (Turing), Bath, Bristol, Cambridge, Exeter, Imperial, KCL, Leeds, Loughborough, Newcastle, QMUL, Sheffield, Southampton, Surrey, Sussex, UCL, Warwick and York, our proposal aims to build on the success of the JADE Tier 2 facility. The current JADE facility represents a unique national resource providing state of the art GPU computing facilities to world leading experts in the areas of Artificial Intelligence/Machine Learning (AI/ML) and molecular dynamics (MD) research. In addition to providing a leading compute resource, the JADE facility has also provided a nucleus around which a national consortium of AI researchers has formed, making it the de facto national compute facility for AI research. By providing a much-needed shared resource to these communities, JADE has also delivered an outstanding level of world leading science, evidenced in the twenty two pages of preliminary case studies submitted to EPSRC on 11/09/18. JADE2 will build upon these successes by providing increased computational capabilities to these communities and delivering a stronger, more robust service to address the lessons learned from the initial service. The architecture for JADE2 will be a similar to that of JADE, based on NVIDIA's DGX platform. JADE is formed from 22x DGX1V nodes. JADE2 will be over twice the size of JADE and employ the more cost effective DGX1 Max Q platform. Differences between Max Q and the premium DGX1V are centred on on a slightly reduced bandwidth to GPU memory and lower peak compute performance. Tests of relevant codes on these platforms show that, for AI/ML and Molecular Dynamics, Max Q achieves at least 3/4 performance, using 2/3 the power for 1/2 of the price. The system will be run as a national facility, providing free access to all academic users through a lightweight Resource Allocation Panel (RAP). HECBioSim will run the RAP for MD users, ATI will run the corresponding RAP for AI/ML users.

Current wireless systems, from Wi-Fi to 5G, have been designed by following principles that have not changed over the last 70 years. This approach has given us dependable, universal wireless connectivity solutions that can deliver any type of digital information. As computing systems substitute universal digital processors with specialised circuits for artificial intelligence (AI), and as wireless connectivity becomes an integral part of the sensing-compute-actuation fabric powered by AI, it is essential to rethink the fundamental principles underpinning the design of wireless systems. The global telecom market is estimated at around USD 850 billion, with the UK telecom industry generating around GBP 30 billion in 2020. The countries that will lead in the creation of the new technological principles and capabilities underpinning 6G will have a significant international market edge, making fundamental research on the subject a critical national policy issue. In this context, neuromorphic sensing and computing are emerging as alternative, brain-inspired, paradigms for efficient data collection and semantic signal processing that build on event-driven measurements, in-memory computing, spike-based information processing, reduced precision and increased stochasticity, and adaptability via learning in hardware. The neuromorphic sensing and computing market was valued at USD 22.5 million in 2020, and it is projected to be worth USD 333.6 million by 2026. Current commercial use cases of neuromorphic technologies range from drone monitoring to the development of fast and accurate COVID-19 antibody testing. NeuroComm views the emergence of neuromorphic technologies as a unique opportunity for the development of efficient, integrated wireless connectivity and semantic processing -- referred to broadly as wireless cognition. Specifically, NeuroComm aims systematically addressing the integration of neuromorphic principles within an end-to-end system encompassing sensing, computing, and wireless communications. The informational currency of neuromorphic computing is not the bit, but the timing of spikes. Neuroscientists have long studied the efficiency and effectiveness of spike-based communications in biological neurons. In the context of wireless cognition, spike-based processing and communication raise novel fundamental questions regarding optimal joint signaling and computing strategies. NeuroComm will take the approach of starting from first, information-theoretic, principles, addressing the problem of what to implement before investigating how to best deploy neuromorphic based wireless cognition. To this end, the project aims at developing an information-theoretic framework for the analysis of wireless cognition systems with neuromorphic transceivers. The efficiency of neuromorphic computing hinges on the co-design of hardware and software. NeuroComm posits that a close integration of neuromorphic computing and communications at the design stage will be needed in order to fully leverage the benefits of brain-inspired wireless cognition. NeuroComm is a collaboration between King's College London (KCL) as lead institution and Princeton University (PU) as academic partner, along with NVIDA, Intel Labs, AccelerComm, and IBM Zurich as industrial partners. The research will build on the PIs' expertise in information theory, machine learning, communications, and neuromorphic computing to explore theoretical foundations, algorithms, and hardware implementation.