Research & Impact

Principal Investigators: Dr Anna Barnes, Professor Sebastian Ourselin

The Artificial Intelligence (AI) in Health and Care Award aims to benefit patients by combining the power of artificial intelligence with the expertise of the NHS to improve health and care outcomes.

The AI in Health and Care Award is increasing the impact of AI systems in helping to solve both clinical and operational challenges across the NHS, including reducing waiting times, improving early diagnosis and saving staff time. It will speed up the most promising technologies through the regulatory process by building an evidence base to demonstrate the effectiveness and safety of artificial intelligence in health and care.

King's Technology Evaluation Centre (KiTEC) was commissioned as an Independent Evaluator to help determine whether selected AI technologies should be adopted across the health and social care system in England. Our health technology evaluations help fill key evidence gaps and accelerate local and national adoption by generating real-world evidence and typically include a health economic assessment and AI acceptability research among patients and healthcare providers.

KiTEC was awarded four Evaluation Partner contracts to assess four AI technologies that are ready for roll out (known as Phase 4 technologies), totalling approx. £2.223m over three years:

• An AI technology designed to aid breast cancer screening
• An AI technology designed to detect cardiac arrhythmias
• An AI technology designed to detect vertebral compression fractures on CT scans
• A multi-tech evaluation of AI tools designed to contouring organs at risk in radiotherapy

Principal Investigators: Professor Serena Counsell

This study will assess the relationship between impaired placental function and altered brain development in CHD by combining customised state-of-the-art neuroimaging in fetuses and neonates with measures of placental oxygenation and perfusion.

We will also undertake neuroimaging and assessments of cognitive and behavioural performance in children with CHD who underwent advanced MR imaging in the newborn period and define early neuroimaging signatures of childhood outcome. This proposal has the potential to elucidate the mechanisms underpinning impaired brain health in survivors of CHD and to provide imaging tools to test efficacy in future trails of neuroprotective treatments. March 2021-Feb 2026 MRC £3.5 million.

Principal Investigator: Professor Gary Cook

PECan: Investigation of a novel 99mTc-labelled single domain antibody (NM01) in the measurement of PD-L1 expression in non-small cell lung cancer and melanoma. The main objectives are to determine how 99mTc-sdAb-PD-L1 (NM01) correlates with tumour immunohistochemical measurement of PD-L1, to determine if baseline PD-L1 expression predicts treatment response and to measure changes in PD-L1 expression during treatment.

PELICan: The primary objective is to measure PD-L1 expression in non-small cell lung cancer with 99mTc-sdAb-PD-L1 and correlate with immunohistochemical measurement on biopsy material. Secondary objectives are to measure safety and intra / inter-tumoural heterogeneity of PD-L1 expression on imaging. Exploratory objectives are to correlate the SPECT/CT PD-L1 assessment with other diagnostic parameters such as blood tumour mutation burden (bTMB) test and to detect the presence of anti-drug antibodies (ADAs) to 99mTc-NM-01.

Principal Investigator: Dr Christos Bergeles

Loss of sight can be reversed with the precise delivery of upcoming regenerative therapies to specific locations within the human retina. The dexterity required, however, well surpasses the capabilities of the human hand. In addition, therapy delivery needs to happen slowly and steadily, even for a duration of 10 minutes. We are creating robotic systems that enable the required dexterity while using image guidance to stabilize themselves.

The project, co-developed with Moorfields Eye Hospital, has received generous support by the National Institutes of Health Research (£1M, 2017 - 2020, and £1.5M, 2022 - 2025), and has brought together research teams from King's College London, University College London, Moorfields Eye Hospital, and University of Kent.

The current funding supports functionalisation of the flexible micro-surgical system with optical fiber-based imaging and force sensing, as well as the creation of a technical file to take the system towards first-in-human evaluation. The overall project is considered for commercialisation and the PI is mentored by King's Entrepreneurship Institute in support of this endeavour.

Principal Investigators: Professor Sebastien Ourselin, Professor John Duncan (UCL)

Epilepsy Navigation (EpiNav) is a software platform to support the planning and execution of neurosurgery for the treatment of patients with epilepsy. We integrate advanced automated image analysis techniques and computer-assisted planning optimisation with an intuitive user interface to enable clinicians to plan procedures using patient specific anatomy.

The program has been supported by: Wellcome Innovator Award (218380/Z/19/Z/) (04/2020-04/2023), Wellcome Trust Health Innovation Challenge Fund (WT106882) (01/2016-06/2020), Health Innovation Challenge Fund (HICF-T4-275, WT 97914).

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Principal Investigator: Professor Elaine Chew

COSMOS uses data science/analytics and citizen science to study and model musical structures created in performance and in time-varying music-like sequences such as cardiac arrhythmia. Music representation of abnormal heart signals has applications in disease stratification.

The autonomic effects of performed music structures has applications in digital music therapeutics. COSMOS is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 788960), 01/06/2019–31/05/2025, €2.5M.

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Principal Investigator: Dr Jonathan Shapey

Brain tumour surgery for low-grade glioma (LGG) improves patient outcome and survival but is very challenging. Even with the most advanced surgical techniques it remains difficult to determine tumour from healthy brain tissue. Advanced surgical methods can use light (fluorescence) created by the tumour to guide surgery. However, in the case of LGG, hardly any fluorescence light is visible, and each surgeon may judge the location and amount of light differently. Recent smart camera systems have the potential to enhance the surgeon’s vision to more reliably identify tumour and healthy brain structures.

Hyperspectral imaging (HSI) is one of the most promising of such technologies providing rich information that is invisible to the human eye and could provide accurate and detailed measurements of tumour fluorescence. Using HSI, this study aims to develop objective surgical guidance to provide detailed real-time tissue margin information during surgery providing a step-change for treating LGG patients. Start date: April 2022. End date: March 2025

Principal Investigator: Dr Thomas Booth

We built an AI model that can reduce reporting times for abnormal examinations by accurately flagging abnormalities at the time of imaging, thereby allowing radiology departments to prioritise limited resources into reporting these scans first. This would expedite intervention by the referring clinical team. The current MRC DPFS grant will enable further finessing of the model and accelerate translation to the clinic. A key objective is to complete validation using multiple prospective hospital data to ensure that the AI model is fit for purpose throughout the UK.

The ARIA trial is a randomised control trial sponsored by NIHR i4i. It is comparing normal standard-of-care fluoroscopy with AI imagine guidance in Aortic Aneurysm Stenting procedures, to see if this new image guidance can reduce the length of operations and the radiation dose from them.

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