Round 2 AI in Health and Care Awards
The winners of the second competition were announced by Secretary of State for Health, Matt Hancock, on 16 June 2021. Read the media release.
The winners are as follows:
Phase 4
Bone Health Solutions
Zebra Medical Vision: A multi-centre project using AI to analyse any CT scan to catch undiagnosed spinal fractures, which can be a marker for osteoporosis. Patients will be directed to fracture prevention programmes, where they will receive lifestyle advice, and medications where appropriate, to reduce future fracture risks associated with the disease.
Paige prostate cancer diagnostic system
University of Oxford: Using AI-based diagnostic software to support the interpretation of pathology sample images, in order to more efficiently detect, grade and quantify cancer in prostate biopsies. This helps address a rise in caseload while there are too few qualified pathologists, which has led to resource shortages in the NHS.
eHub
eConsult Health Ltd: Using AI to intelligently triage and automate GP e-consultation requests, reducing staff time to manage the system. eHub aims to improve clinician efficiency, and allow easier interface for GPs and admin staff with eConsult software, reducing errors and improving patient safety.
DERM
Skin Analytics Ltd: Designing innovative pathways, leveraging AI in the analysis of images of skin lesions, distinguishing between cancerous, pre-cancerous and benign lesions. DERM sets out to highlight the most likely cancers, and aid in swift and appropriate treatment being offered, reducing backlogs in this service and reducing premature deaths.
Phase 3
CaRi-HEART
Caristo Diagnostics Ltd: Using AI to detect the invisible signatures of inflammation in the heart from regular CT scans. This gives a better prediction of the risk of cardiovascular disease, allowing more efficient targeting of medication and treatment.
Cogstack Natural Language Processing
King’s College London: This AI-based clinical coding of medical records aims to enable more efficient and accurate analysis, free up staff time, and improve research. Recruitment for clinical trials will be improved, and individual clinicians will be able to analyse patient records more efficiently.
qER
Qure.ai Technologies Private Limited: Evaluation of the use of AI to support emergency department clinicians to analyse CT scans in patients with head injuries, leading to faster treatments and better outcomes for the patients. This can be vital in areas where there is a shortage of trained radiologists to analyse the scan images immediately, particularly during after-hours.
ArtiQ.Spiro
Guy’s and St Thomas’ NHS Foundation Trust: Testing the use of AI to interpret and evaluate the spirometry test used to determine lung function, freeing up clinician time, and reducing incorrect diagnoses. Part of the NHS’s Long Term Plan to combat lung disease, and reduce health inequality.
Workforce deployment solutions
Navenio Limited: Using AI to implement workforce solutions, ensuring that both logistics and clinical support teams are in the right place at the right time within a hospital, to maximise efficiency. Built on Oxford University-originated and infrastructure-free indoor location AI technology that simply uses smartphones to sensitively automate the deployment of teams.
Analysing breast screening X-rays
Imperial College London: Evaluating the potential of AI for analysing X-ray images of routine mammograms (breast screening). This will improve accuracy, safety, cost-effectiveness and patient experience, giving results faster, and helping mitigate the shortage of radiographers available to analyse mammograms.
Open-source AI to augment and accelerate radiotherapy workflows
Cambridge University Hospitals NHS Foundation Trust: Cambridge University Hospitals NHS Trust and University Hospitals Birmingham NHS Trust are leveraging open source AI tools from Microsoft Project InnerEye to differentiate between tumour and healthy tissue on cancer scans (called ‘segmenting’), prior to radiotherapy treatment. The aim is to evaluate how this could save clinicians’ time, with the potential to reduce the time between the scan and starting treatment.
DOLCE
Optellum Ltd: Optellum’s AI decision support helps doctors make optimal decisions for patients with potentially cancerous lung lesions found in CT scans. The aim is to reduce the time to cancer treatment, increase survival rates, and reduce unnecessary invasive procedures.
Lenus COPD Management Service
Storm ID Ltd: A digital healthcare service that shifts the management of COPD patients to a proactive and preventative care model. It uses AI to analyse output from patients’ daily monitoring and wearable devices to predict deterioration, and enable targeted intervention by care teams for those patients at most risk.
Wysa
WYSA Ltd: Real-world testing of an AI app as an early intervention and support tool for mental health, to be used by patients on the waiting list for regular care. The aim is to reduce symptoms of anxiety and depression, and detect people experiencing severe mental health difficulties, so that they can be prioritised for treatment.
Phase 2
MyDiabetes IQ
MyWay Digital Health Ltd: MyWay Digital health is testing an AI tool for predicting diabetes complications, sub-type diagnosis, and treatment choices, to support clinicians including non-specialist GPs with managing their diabetes patients. The aim is to prevent complications, like heart attacks and foot ulcers.
AI systems for precision blood group matching
University of Cambridge: This project will develop AI-systems for genetic blood group typing, automated stocking of blood of different types, and precision matching of patients to blood units. The new AI systems aim to transform the quality and efficiency of blood matching, reduce complications of blood transfusions, and improve clinical care for patients.
Advance notice of deterioration in cystic fibrosis
Royal Papworth Hospital NHS Foundation Trust: This project is using AI with home-monitoring to predict sudden dips in the health of adults with cystic fibrosis, enabling early intervention and supporting patients to stay well without repeated hospital check-ups.
mySmartCOPD
University of Southampton: Patients with Chronic Obstructive Pulmonary Disease (COPD) are being supported to use home monitoring of various health markers, and report them using the MyCOPD app. The data are analysed by AI to predict ‘exacerbation events’, where a patient’s condition suddenly declines, in order to prevent or lessen these events.
ImageDx
Sonrai Analytics Ltd: A centralised, AI-based solution for faster and more accurate testing on cancer biopsy tissue for colorectal, lung and other cancers.
First PLUS
Perspectum Ltd: The First PLUS project, led by University of Oxford, Perspectum Group and The Fetal Medicine Foundation, uses AI to analyse the size of the placenta during the first trimester as a predictor for Fetal Growth Restriction, a risk factor for stillbirth and other neonatal conditions.
CHRONOS
University of Oxford: A machine learning-based clinical decision support for ‘digital triage’ in secondary mental health care. CHRONOS will be able to review a patient’s electronic medical record alongside their referral documents, to deliver a summary of relevant clinical information and suggest a suitable treatment team. This will assist secondary mental healthcare teams to identify appropriate care in a timely, safe and transparent way.
CESCAIL
CorporateHealth International UK Ltd: The CESCAIL project is testing how effective AI can be in performing preliminary analysis on the hours of images taken during capsule endoscopy, saving clinicians up to 80% of the time they would usually spend on this work. The project will allow this more flexible type of endoscopy to be rolled out further in the community.
Eye2Gene
University College London Moorfields Ophthalmic Reading Centre: Eye2Gene is exploring the use of AI to determine which genetic condition is causing a patient’s inherited retinal disease, by examining eye scans. With more than 300 possible genetic causes, requiring differing management or treatment options, swift diagnosis is crucial.
Phase 1
Issues and themes analysis in complaints
Methods Analytics Ltd: This project aims to use AI and Natural Language Processing to improve the speed, responsiveness and learning from the management of healthcare complaints, picking up key issues in individual cases, and recurring patterns across a service or area.
Machine learning to improve the diagnosis of heart attacks
University of Edinburgh: This project is developing an AI-guided tool to help doctors and nurses interpret a patient’s troponin levels to diagnose heart attacks more accurately. A web app can be used on a mobile device at the bedside or embedded into hospital computer systems.
Monitoring slow-growing brain tumours
University of Cambridge: Certain types of brain tumour are deemed low-risk, as they grow so slowly. This project aims to develop AI to measure the volume of tumours from scans, and learn which are at risk of growth, to ensure those patients are monitored more frequently, and others can be reassured that their tumour is lower risk.
Pathpoint Detect
Open Medical Ltd: Pathpoint Detect is a novel, transparent decision support tool for image-based diagnosis in dermatology. This can be integrated directly into the Pathpoint suite of care pathway management and clinical workflow solutions.
Developing the Blood Pressure Index for improved blood pressure control
Imperial College London: Developing the Blood Pressure Index will provide the public with more useful and AI-driven blood pressure data for self-monitoring, in order to reduce high blood pressure, which is the leading cause of strokes, heart disease and deaths.
panPIERS
King’s College London: This project plans to combine the existing PIERS (Pre-eclampsia Integrated Estimate of Risk Score) tools, miniPIERS and fullPIERS, together with AI, into an app to calculate an individual woman’s risk of the complications of pre-eclampsia, including following birth.
PREVAIL – PhototheRapy Enhanced Via Artificially Intelligent Lasers
University of Southampton: The PREVAIL project is developing automated techniques for the treatment of psoriasis by the targeted delivery of laser light. This could reduce the risk of skin cancer in adjacent skin caused by current treatments with UVB, where the unaffected as well as affected skin has often been exposed to UVB rays.
Measuring hip dysplasia in children with cerebral palsy
University of Manchester: This project seeks to use machine learning to assess X-ray images of the hips of children with cerebral palsy, to determine whether they are at risk of hip dislocation, a process which can be time-consuming when carried out by clinicians.
CirrhoCare
Cyberliver Ltd: CyberLiver proposes using AI to examine the factors that influence the development of new liver-related complications and clinical outcomes in patients with advanced cirrhosis, in the hope of developing a predictive tool to guide whether patients will benefit from early hospital intervention or continued care at home.
Decision-making for less-than-perfect kidney transplant matches
University of Oxford: Deceased kidney donors often have pre-existing medical problems that can affect the outcome for recipients after transplant. The decision whether to accept a kidney offer, or wait for one which is potentially better, can be challenging for both surgeon and patient. This project aims to train AI to predict the outcome for a patient, to aid in decision-making.
Predicting pre-term labour using AI
Coventry University: This project will explore the use of electrohysterography sensing to predict the pre-term labour of women giving birth before 37 weeks, using AI to provide more accurate data than is currently available.
R-CANCER
Imperial College London: R-CANCER will improve the quality of decisions made by doctors when deciding how best to detect and diagnose cancer, by intelligently collating, analysing and interpreting new data on cancer from academic and open data sources.
Diagnosis of ‘glue ear’ with AI
Cardiff Metropolitan University: This project aims to test the use of AI to accurately diagnose ‘glue ear’ (Otitis Media with Effusion) in children, preventing delayed or incorrect diagnoses, and reducing complications and recurrent issues.