A team of researchers from the University of Edinburgh and Heriot-Watt University are developing artificial intelligence (AI) and socially assistive robots to detect urinary tract infections (UTIs) earlier.
The FEATHER project aims to reduce the number of serious adverse outcomes that can result from late or misdiagnosis and reduce the amount of antibiotics that are prescribed while clinicians wait for lab results.
The ground-breaking research has been awarded £1.1 million from the UK Government by the Engineering and Physical Sciences Research Council, part of UK Research and Innovation, and the National Institute for Health and Care Research (NIHR).
UTIs affect 150 million people worldwide annually, making it one of the most common types of infection. When diagnosed early, it can be treated with antibiotics. If left untreated, UTIs can lead to sepsis, kidney damage and even loss of life.
Diagnosis, however, can be difficult with lab analysis, a process taking up to 48 hours, providing the only definitive result. Early signs of a UTI can also be challenging to recognise because symptoms vary according to age and existing health conditions. There is no single sign of infection but a collection of symptoms which may include pain, temperature, frequency of urination, changes in sleep patterns and tremors.
UTIs are particularly difficult to diagnose in people receiving formal care, and there is significant antibiotic overtreatment in this group as clinicians wait for lab results to return.
To address these concerns, researchers from the University of Edinburgh and Heriot-Watt University are working with two industry partners from the care sector. Scotland’s national respite centre, Leuchie House, and Blackwood Homes and Care are providing user insights to help researchers develop machine learning methods and interactions for socially assistive robots to support earlier detection of a potential infection and raise an alert for investigation by a clinician.
The project will gather continual data about the daily activities of individuals in their home via sensors that could help spot changes in behaviour or activity levels and trigger an interaction with a socially assistive robot. The FEATHER platform will combine and analyse these data points to flag potential infection signs before an individual or carer is aware there is a problem. Behaviour changes could include kettle use, change in walking pace, cognitive function through interaction with a socially assistive robot or a change to sleep patterns.
The AI and implementation aspects of the project will be led by Professor Kia Nazarpour, Dr Nigel Goddard and Dr Lynda Webb from the University of Edinburgh. The Human Robot Interaction aspects will be led by Professor Lynne Baillie, assisted by Dr. Mauro Dragone, from Heriot-Watt University.
Professor Kia Nazarpour, project lead and Professor of Digital Health at the School of Informatics, University of Edinburgh, said: “This unique data platform will help individuals, carers and clinicians to recognise the signs of potential urinary tract infections far earlier, helping to prompt the investigations and medical tests needed. Earlier detection makes timely treatment possible, improving outcomes for patients, lowering the number of people presenting at A&E, and reducing costs to the NHS.
“We also believe it will help to minimise the amount of antibiotics that are necessarily prescribed as a cover while waiting for lab results. As the second most common reason for the prescription of antibiotics, the infection makes a significant contribution to the increasingly concerning problem of drug-resistant bacteria, and there is widespread advantage to society in implementing better diagnosis.”
Professor Lynne Baillie, lead for the National Robotarium on Human-Robot Interaction, Assistive Living and Health, said: “We hope this work will create an additional structured support mechanism for people who live independently. Studies show that there is a significant association between delirium and UTI in older adults and, while it is possible that carers will pick up these signs, we should not be relying on observations alone. We are working with stakeholders to co-design the robot interaction and data collection for the machine learning methods to better support longer and healthier independent living.
“Working sensitively and supportively with this vulnerable social group is of the utmost importance. By developing the technology in the new Assisted Living Lab at the National Robotarium, we are able to test it in a realistic social care setting.”
Kitty Walker, a care receiver and regular guest at Leuchie House, said: “The impact of having a UTI can be far more serious than a lot of people may realise. Commonly, my speech becomes affected which can make it difficult to communicate with people like I normally would. More seriously, I’ve been hospitalised in the past after the late diagnosis of a UTI led to me having a seizure and I required mouth-to-mouth resuscitation.
“It can often take a long time to receive a full diagnosis and be given the right antibiotics to tackle the infection. In the meantime, I’m usually prescribed a general antibiotic until my results come back. Being able to spot the early indicators that I have a UTI would save any anxiety I might feel when I know there is a problem and help reduce the number of different antibiotics I need to take.”
UK Government Minister for Scotland, Malcolm Offord, said: “Data and AI have the potential to transform diagnosis and treatment of so many conditions and improve outcomes for patients.
“This research will make a big difference to detecting UTIs as quickly as possible, and I am glad residents in Scotland’s care sector will be some of the first to benefit.
“The UK Government is providing £1.1 million of research funding for this project, and through the City Deal we are investing £21 million in the new National Robotorium facilities at Heriot-Watt University.”
Scottish Government Business Minister, Ivan McKee, said: “I am pleased to see this ground-breaking and innovative work being carried out in Scotland. Through allowing earlier diagnosis and treatment of UTIs, this AI and robotics research can make a key contribution to improving health and social care provision in Scotland, while ensuring the dignity of individuals is protected.
“The National Robotarium and its Ambient Assisted Living lab will be a key asset for Scotland, and the UK, in supporting people to live well and independently in their communities as they age. The Scottish Government is investing £1.4 million in the National Robotarium as part of our wider investment in the £1.3 billion Edinburgh and South East Scotland City Region Deal.”
Colin Foskett, Head of Innovation & Research at Blackwood Homes and Care, said: “Understanding how socially assistive AI can be used to better detect UTIs has the potential to improve the health & wellbeing of our customers. Early UTI detection could prevent hospital admissions, associated decline and ensure people can continue to live independently for longer.
“Building on our existing collaborations with the University of Edinburgh and Heriot-Watt University, this exciting new project will co-design and co-create new products and services with our customers, our staff and the academic partners. We look forward to exploring this emerging technology and are excited to see the potential of AI for the future of independent living.”
Mark Bevan, CEO at Leuchie House, said: “The personal, health and financial cost of urinary infections are massive, costing the NHS at least £500M last year, devastating people’s lives and adding great complexity to the provision of increasingly complex adult care.
“Leuchie House is uniquely placed as a national centre with unrivalled access to guests who trust us to manage the sharing of their health data and experience.
“This ground-breaking partnership with guests, the University of Edinburgh and Heriot-Watt University, is just one example of how we are reaching out from our base in East Lothian to improve the lives of people across Scotland and beyond, developing important new knowledge through our practice experience and partners research expertise.”
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